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Household activity-travel behavior : implementation ofwithin-household interactionsAnggraini, R.
DOI:10.6100/IR657789
Published: 01/01/2009
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Household Activity-Travel Behavior: Implementation of Within-Household
Interactions
PROEFSCHRIFT
ter verkrijging van de graad van doctor aan de
Technische Universiteit Eindhoven, op gezag van de
rector magnificus, prof.dr.ir. C.J. van Duijn, voor een
commissie aangewezen door het College voor
Promoties in het openbaar te verdedigen
op dinsdag 1 december 2009 om 16.00 uur
door
Renni Anggraini
geboren te Banda Aceh, Indonesië
Dit proefschrift is goedgekeurd door de promotor:
prof.dr. H.J.P. Timmermans
Copromotor:
dr. T.A. Arentze
Copyright © 2009 R. Anggraini
Technische Universiteit Eindhoven,
Faculteit Bouwkunde, Urban Planning Group
Photo by: Aldy Fithrico
Cover design: Tekenstudio, Faculteit Bouwkunde
Printed by the Eindhoven University of Technology Press Facilities
BOUWSTENEN 141
ISBN 978-90-6814-623-4
NUR-code 955: Bouwkunde
i
PREFACE
This thesis is the result of my PhD study that I have accomplished as a member
of the Urban Planning Group, Eindhoven University of Technology. Without
the help, contribution and support of many people, family, friends and
colleagues, I would not have been able to complete this PhD research project. I
would like to thank everyone who has supported and assisted me during this
time, and especially express my gratitude to those who have assisted me by
providing valuable feedback on my work at various stages.
First of all, it is a great honor for me to have worked under the supervision of
Professor Harry Timmermans. I acknowledge and show my profound respect to
him as a highly reliable advisor. He is a very encouraging and inspirational
advisor, always providing interesting and promising research directions. I
would also like to thank my co-promoter, Theo Arentze, for his considerable
support. Throughout my study, Theo provided me very detailed technical and
conceptual support both in theoretical and practical aspects, especially in
computer programming. As my research concerned the refinement of the
ALBATROSS system, it was not an easy task for me to understand somebody
else’s work and algorithms. The bi-weekly meetings with Harry and Theo have
improved my knowledge of activity-based analyses and research in general.
Their feedbacks and comments on papers and the thesis manuscript were very
impressive and improved my English writing skills. Without their assistance, it
would have been impossible for me to finalize this PhD research. Thanks to
Harry and Theo! I really loved working with both of you.
I would like to thank the University of Syiah Kuala for financing my PhD
research through the TPSDP Project-Dikti during my first two years. Special
gratitude goes to Prof. Dr. Samsul Rizal, Dr. Alfiansyah Yulianur, Dr. Mustanir,
Dr. Ismail AB, Dr. Moch. Afiffudin, Danker Schaareman, and all staff for their
efforts to the successful of my research. I would also like to express thank the
Eindhoven University of Technology for financially supporting me for the
second half of my PhD research.
ii
I would also like to thank my colleagues in the Urban Planning Group. I
enjoyed pie time, lunch time and chatting on many occasions. In particular, I
show my appreciation to Mandy van de Sande-van Kasteren, Anja van den
Elsen-Janssen and Ingrid Dekkers-de Bruijn for their splendid secretarial
support and kindness, and other colleagues including Astrid Kemperman and
Aloys Borgers for their inspiration. I will never forget the help of Peter van der
Waerden and Leo van Veghel who picked me up at Schiphol airport on day one.
Thanks also go to my colleagues, Marloes Verhoeven, Claudia Pelizaro, Linda
Nijland, and Han Qi who were very generous giving away their home stuff. I
would also like to thank my dear friends and family, Ina Rosyid, Inne Harjanto,
Dianti-Oki, and Luluk-Nandra who were welcoming my children to their
homes, especially when my husband was away to Indonesia and I could not pick
up the children from school. Thanks also to Ella Meilinda and Rinaldi Husin
families who visited us frequently in Eindhoven and made our stay in Holland
more cheerful. It was also a sweet memorable time with Vivi, Desi, Dianti, Runi
and Leila for cooking together during Ramadhan. Especially to Desi and Ferdi:
thanks a lot for guiding me in computer programming.
Special thanks also go to my brothers and sister, Yudi Kurnia, Susi Andriani,
and M. Fadhilla Ismali who always supported me in every possible way, and to
my mother and my late father, for giving me everlasting support and pray for
my education and life. I thank God for having all of you in my life. Thanks also
to my parents-in-law for support and kindness. Last of all, I would like to thank
my dearly-beloved husband, Aldy Fithrico, and our lovely kids, Alyauma
Akmal Kalani and Alzhira Hana Fitriani. Their presence and love were really
delightful and allowing me to enjoy our time in Holland. Thanks for all support,
especially during the injury time of finalizing the thesis, when my husband and
son helped me to produce the author and subject indexes.
Finally, I thank the many people who contributed to my life and ask forgiveness
from those I have omitted unintentionally. Thank you all!
iii
TABLE OF CONTENTS
Preface
List of Figures
List of Tables
CHAPTER 1
INTRODUCTION 1
1.1 Shifting Paradigms in Travel Demand Modeling 1
1.2 Household Decision Making 3
1.3 Aims and Outline of the Thesis 3
References 6
CHAPTER 2
LITERATURE REVIEW 7
2.1 Introduction 7
2.2 Analytical Studies on Household Decision Making 8
2.2.1 Car Allocation and Usage Decisions 8
2.2.2 Task and Time Allocation Decisions 8
2.2.3 Joint Activity Participation 13
2.3 Partial Models of Household Decision Making 15
2.3.1 Car Allocation 15
2.3.2 Task Allocation 16
2.3.3 Joint Activity Participation 23
2.3.4 Travel Arrangements 24
2.4 Household Decision Making in Comprehensive Activity-Based
Models 26
2.4.1 Constraint-based Models 27
2.4.2 Simulation Models 27
2.4.3 Utility-Maximizing Models 29
2.4.4 FAMOS 30
2.4.5 CEMDAP and CEMDAP-2 31
2.5 Conclusions 32
References 32
iv
CHAPTER 3
FRAMEWORK 44
3.1 Introduction 44
3.2 ALBATROSS and Household Decision Making 45
3.3 The New Version of ALBATROSS 46
3.3.1 Activity-Travel Diary Data 48
3.3.2 The ALBATROSS Process Model and Extension to
Include Household Decision Making 51
3.3.2.1 The Mandatory Activity Module 57
3.3.2.2 The Non-Work Activity Module 59
3.4 Derivation of Decisions from Decision Tree 60
3.4.1 Discrete Choices 60
3.4.2 Continuous Choices 61
3.4.3 Goodness-of-Fit Measures 63
3.4.3.1 Discrete Choices 63
3.4.3.2 Continuous Choices 65
3.5 Conclusions and Discussion 65
References 66
CHAPTER 4
CAR ALLOCATION BETWEEN HOUSEHOLD HEADS IN
CAR-DEFICIENT HOUSEHOLDS: A DECISION MODEL 68
Abstract 68
4.1 Introduction 69
4.2 ALBATROSS Process Model 71
4.3 Data 74
4.4 Car Allocation Model Specification 74
4.5 Empirical Analysis 77
4.5.1 Descriptive Analysis 77
4.5.2 Decision Tree Induction 79
4.5.3 Deriving Impact Tables 80
4.5.4 Condition and Action Variables 82
4.5.5 Results 84
4.6 Summary and Conclusions 90
References 91
v
CHAPTER 5
MODELING JOINT ACTIVITY PARTICIPATION AND
HOUSEHOLD TASK ALLOCATION 93
Abstract 93
5.1 Introduction 94
5.2 The Activity Scheduling Process Model 95
5.3 Models Specification 97
5.3.1 Activity Selection 97
5.3.2 Activity Allocation 98
5.4 Data 98
5.5 Analyses 98
5.5.1 Decision Tree Induction 98
5.5.2 Deriving Impact Tables 99
5.5.3 Condition and Action Variables 101
5.5.4 Results: Activity Participation Tree 104
5.5.5 Results: Task Allocation Tree 107
5.6 Conclusions and Discussion 109
References 111
CHAPTER 6
CONTINUOUS CHOICE MODEL OF TIMING
AND DURATION OF JOINT ACTIVITY 112
Abstract 112
6.1 Introduction 113
6.2 Overview of ALBATROSS Model 114
6.3 Data Description 117
6.4 Variable Specification 119
6.5 Methods 123
6.5.1 Decision Tree Induction 123
6.5.2 Deriving Impact Tables 124
6.6 Results 124
6.7 Conclusions and Discussion 127
References 129
CHAPTER 7
HOUSEHOLD LOCATION CHOICE MODELS FOR
INDEPENDENT AND JOINT NON-WORK ACTIVITY 131
Abstract 131
7.1 Introduction 132
vi
7.2 Location Decisions in the Existing Model 133
7.3 Household Location Decisions (Joint Activity) 136
7.4 Data 138
7.5 Overview of Condition and Action Variables 138
7.6 Decision Tree Induction and Impact Table Methods 142
7.7 Descriptive Analysis 143
7.8 Results 145
7.8.1 Independent Activity 146
7.8.2 Joint Activity 148
7.9 Conclusions 149
References 151
CHAPTER 8
CAR ALLOCATION DECISIONS IN CAR-DEFICIENT
HOUSEHOLDS: THE CASE OF NON-WORK TOURS 153
Abstract 153
8.1 Introduction 154
8.2 Data Description 155
8.3 Methodology 155
8.3.1 Car Allocation Decisions 155
8.3.2 Decision Tree Induction 159
8.3.3 Impact Tables 160
8.3.4 Condition and Action Variables 160
8.4 Descriptive Analysis 163
8.5 Results 165
8.6 Conclusions 168
References 170
CHAPTER 9
THE INTEGRATION MODEL 171
9.1 Introduction 171
9.2 Test of Validity Using MON Data 172
9.2.1 Frequencies 172
9.2.2 Indicators 176
9.3 Test of Sensitivity 178
9.3.1 Synthetic Populations 178
9.3.2 Scenario 178
9.4 Conclusions and Discussion 188
vii
CHAPTER 10
CONCLUSIONS AND DISCUSSION 190
SUMMARY 194
Appendix 199
Author Index 247
Subject Index 250
List of Publications 255
Curriculum Vitae 257
viii
List of Tables
TABLE 3.1 Classification of Activities in a Household in ALBATROSS 49
TABLE 3.2 Socio-Economic and Situational Attributes used in
ALBATROSS 49
TABLE 3.3 Accessibility Measures used in ALBATROSS 50
TABLE 4.1 Defining Car Allocation Decisions in Households 75
TABLE 4.2 Distributions of Households across Household Composition
and SEC (%) 77
TABLE 4.3 Distributions of Household Heads across Household
Composition and Work Status of Household Heads by
Gender (%) 78
TABLE 4.4 Work Duration Statistics by Work Status and Gender 78
TABLE 4.5 Work Duration Statistics by Day of the Week and Gender 78
TABLE 4.6 Condition Variables for Car Allocation Model 83
TABLE 4.7 Frequency Distribution of Work Status across the Action
Variables 85
TABLE 4.8 Confusion Matrix for the Training and Validation Sets 89
TABLE 4.9 Impact Tables of Condition Variables of Car Allocation
Model 89
TABLE 5.1 Activity Classifications in a Household 96
TABLE 5.2 Condition Variables for Decision Tree Models 102
TABLE 5.3 Impact of Condition Variables of HH Activity Participation
Model 105
TABLE 5.4 Impact of Condition Variables of Task-Activity Allocation
Model 108
TABLE 6.1 Independent and Joint Activity Frequency (percentage) 117
TABLE 6.2 Average Duration (minutes) 118
TABLE 6.3 Definitions of Condition Variables 121
TABLE 6.4 Duration Tree Model 126
TABLE 6.5 Start-Time Tree Model 128
TABLE 7.1 Condition Variables of Independent and Joint Activity 140
TABLE 7.2 The Percentage of Performing Independent Activity at the
Same Location as Previous and/or Next Activity 143
TABLE 7.3 The Percentage of Performing Joint Activity at the Same
Location as Previous and/or Next Activity 144
ix
TABLE 7.4 The Percentage of Performing Independent and Joint Activity
by Available Distance and Location Size Band in Prisms 144
TABLE 7.5 Results of Location Decision Tree Models 145
TABLE 7.6 Impact Table for Independent Activity 147
TABLE 7.7 Impact Table for Joint Activity 149
TABLE 8.1 Itinerary of Male-Female Heads in a Particular Household 159
TABLE 8.2 Condition Variables for Car Allocation Model 161
TABLE 8.3 Primary Activity of a Tour of Male – Female 164
TABLE 8.4 Percentage of Getting a Car by Male/Female across Work
Status 164
TABLE 8.5 Average Duration of Non-work Tour(s) across Work Status
(in minute) 164
TABLE 8.6 Results of the Car Allocation Model to Non-Work Tours 166
TABLE 8.7 Impact Table of Car Allocation Decision to Non-Work Tour
Model 166
TABLE 9.1 Some Relevant Variables at the Aggregate Level 174
TABLE 9.2 Observed and Predicted of the Old and New Versions 180
TABLE 9.3 Comparison between Base-line and Scenario on
Socio-Demographic Characteristics 181
TABLE 9.4 Predicted Scenario Effects on Some Variables/Indicators:
Old Model Version 182
TABLE 9.5 Predicted Scenario Effects on Some Variables/Indicators:
New Model Version 184
x
List of Figures
FIGURE 3.1 Main Steps in the Scheduling Process of Current
ALBATROSS 53
FIGURE 3.2 Generation Modules in ALBATROSS 53
FIGURE 3.3 The Process Model for Mandatory Activities 54
FIGURE 3.4 The Process Model for Predicting Locations of Work
Activities 55
FIGURE 3.5 The Process Model for Predicting Locations of Work-Related
and Non-Work Activities 55
FIGURE 3.6 The Process Model for Non-Work Activities 56
FIGURE 4.1 Schematic Representation of Main Steps of the ALBATROSS
Process Model 72
FIGURE 4.2 The Process of Car Allocation Model for Work Tours 75
FIGURE 4.3 Examples of Distinguished Cases 76
FIGURE 4.4 Car Allocation Tree Model with 5 Major Branches 87
FIGURE 6.1 Household Activity-Travel Scheduling Process of
ALBATROSS 116
FIGURE 6.2 Start-Time Profiles every 30 minutes for each Activity 118
FIGURE 7.1 The Process Model for Predicting Location of Non-Work
Activities 135
FIGURE 8.1 The Process of Car Allocation Decisions for Non-Work
Tour 157
FIGURE 8.2 An Example of Defining Car Allocation Decision Cases in
Household Schedules 159
1
Chapter 1
INTRODUCTION
1.1 SHIFTING PARADIGMS IN TRAVEL DEMAND MODELING
Travel demand modeling has been considered a fundamental area in transportation
research for decades. It has been customarily used in urban planning and transportation
engineering to predict transport demand and evaluate the possible consequences of
spatial, infrastructure, and socio-economic policies. The traditional paradigm, still
dominant in planning practice, is the trip-based, four-step modeling approach. The
four-step model is a primary tool for forecasting future demand and performance of a
transportation system. In order to assess the impact of infrastructure investments and
other policies, models that predict long term travel demand were deemed critical in
evaluating alternative investment and other policies. The four-step model is achieving
this goal by breaking down the decisions that ultimately lead to traffic flows into trip
generation, destination choice, choice of transport mode and route choice. These four
subsequent decisions are modeled separately and independently. Originally, traffic
zones served as the unit of aggregation; later travel behavior of individuals was
simulated. Predicted flows are then used to determine future road capacity needs. For
more details see Ortuzar and Willumsen (1994) and McNally (2007).
In the 1970s, increasing concerns were raised about these four-step models, which
were criticized for their lack of theoretical appeal and lack of modeling many
2
interdependencies that may exist among the various choice facets. The model forecasts
turned out to be very unreliable and failed to assess especially the secondary effects of
policy measures correctly. The models lacked any explanation in terms of human
decision making. Furthermore, the models disregarded constraints such as intra-
household constraints, situational constraints, space-time constraints, and institutional
constraints. They also neglected the dependencies between travel mode, departure time
and destination choice.
When in the 1990s, policy shifted from long-term investment strategies to short-term
market-oriented solutions, the need to develop transport demand models that could
predict behavioral responses to policy measures was expressed in the academic
research community and was somewhat echoed in policy agendas. It led to the
development of activity-based models, which view travel as the result of people
organizing their activities in time and space. Activity-based models are founded in
behavioral theory and focus on the interdependencies between activity generation,
transport mode choice, destination, stop pattern and route choice, in the context of
multiple constraints that limit the choices of individuals and households. Moreover,
temporal dimensions were added to increase the sensitivity of the model. Activity-
based models also predict the timing and duration of activities.
While the vast majority of planning organizations continue to rely on traditional
models, academic research suggests that activity-based approaches promise greater
predictive capability, more accurate forecasts, and especially more realistic sensitivity
to policy changes (McWethy, et al., 2002). Recognition of the various
interdependencies in activity timing and other travel attributes allow greater realism in
models of travel demand. Moreover, activity-based modeling is better suited to current
transportation planning interests. In general, activity-based models focus on activities
as the unit of analysis as opposed to trips as the unit of analysis in trip-based models.
This shift has enabled the models to address issues related to substitution of non-travel
alternatives. Focusing on activity episodes also permits the incorporation of constraints
such as time constraints related to opening hours, work schedules, expected activity
duration, and multi-day scheduling of activities.
Different modeling approaches have been suggested in the literature, and each of these
has led to operational models. The dominant approach is based on the principle of
utility-maximization and corresponding discrete choice models. Observed activity-
travel patterns are viewed as the results of individuals maximizing their utility. The
potential of discrete choice models has been recognized from the mid 1970s onwards.
The shift from trip-based via tour-based to activity-based models simply meant
3
increasing complexity. Typically some nested structure is assumed and the parameters
of the model are estimated using the principle of utility-maximizing behavior.
A second approach is focusing primarily on time use. An example is AMOS/PCATS
(Kitamura and Fujii, 1998), which was later operationalized for the State of Florida
(FAMOS). CEMDAP, developed by Bhat and his co-workers (2004) is an example of
a hybrid system. It consists of a series of separate submodels, which are linked in a
micro-simulation system. Each submodel applies advanced econometrics.
Finally, rule-based models have been developed. These models assume that choices are
context-dependent. Logical rules are extracted from empirical observations for each
stage of an assumed process model. An example is ALBATROSS (Arentze and
Timmermans, 2000, 2004, 2005) which has been developed for the Dutch Ministry of
Transportation, Public Works and Water Management.
1.2 HOUSEHOLD DECISION MAKING
The aim of introducing more interdependencies in the models is not only concerned
with interdependencies in choice facets, but also with interdependencies between the
decisions of individuals. It was realized that in many cases, it is not the individual, but
rather the household that makes decisions. Households are relevant in at least three
situations. First, the activity-travel patterns of household members need to be
synchronized in time and space for joint activities, such as dinner or a family outing.
Second, resources may need to be allocated to individual household members. In turn,
resource allocation decisions may limit subsequent choices of individual members. For
example, if one member uses the car in a single-car household, other household
members cannot use the car at the same time, implying their action space may be
limited. Thirdly, some activities are household activities, implying that only one
household member has to conduct that activity. In turn, such task allocation decisions
influence other aspects of activity-travel programs.
An examination of the literature shows that although the topic of household decision
making has been high on the research agenda for many years, most activity-based
models of transport demand are still based on individual travel patterns.
1.3 AIMS AND OUTLINE OF THE THESIS
An examination of the literature shows that although the topic of household decision
making has been high on the research agenda for many years, most activity-based
4
models of transport demand are still based on individual travel patterns. The goal of
this thesis therefore is to systematically model household decision making processes in
an activity-based framework with a special focus on resource and task allocation and
joint activity participation. More specifically, this study represents an attempt to
improve ALBATROSS (Arentze and Timmermans, 2004). This model was one of the
few of its generation that did include aspects of household decision making by
simulating the decisions of one household member, and then based on the outcome of
this, model the decision process of another household member.
The aim of this thesis is primarily to refine the ALBATROSS model to represent
household-level decision making more explicitly so that the interaction between
persons can be captured well. In particular, the following elements will be further
elaborated:
1. Joint activity participation choice was not modeled in the sense that the required
synchronization in case of joint activities was not imposed as a constraint. We
will attempt to model joint activity participation in a more consistent manner.
2. Activity allocation to each household head was an implicit decision step. In this
study, we will model this step explicitly.
3. Car allocation to each male and female head was also an implicit decision, in
particular for those households with more drivers than cars. The car allocation
problem will now also be modeled explicitly in this study.
The aim of refining ALBATROSS is to make it more comprehensive and applicable
for household-level decision making. Household heads need to trade-off activity needs
and mobility in the context of joint activity participation, household task allocation and
mobility. Joint activity participation needs compromise when the activity is done by
male and female jointly.
In order to achieve these goals, some component of the process model underlying
ALBATROSS is elaborated or re-designed in terms of household decision making.
Moreover, a new much larger data set (the MON-data) is used, implying that the
decision rules that are derived are based on more (household) data. The structure of this
thesis follows the process model underlying ALBATROSS.
Before going through the chapters that make up this thesis, it should first be noted here
that most chapters are based on previously published conference papers or journal
articles. Intrinsically, this format leads to some overlap between some of the chapters,
especially regarding parts of introduction, methods used and descriptions of data
5
collection efforts, although every attempt has been made to write each paper in such a
way that transitions from one chapter to another are as smooth as possible.
Chapter 2 starts by discussing the literature review in the area of household decision
making in urban travel demand modeling. Chapter 3 further continues to discuss the
general framework underlying the model system. It motivates the potential advantages
of a rule-based system. In terms of modeling, the challenge is to extract decision rules
from observed activity-travel patterns. Throughout the thesis, a CHAID-based decision
tree induction method is used as in the basic model. Chapter 4 describes the results of
the first household decision. It is concerned with the problem of car allocation decision
to work tours focusing on car-deficient households, i.e. households where the number
of drivers is higher than the number of cars, the decision who or no-one at all uses the
car to go to work is modeled.
Chapter 5 discusses the results of the model for generating non-work activities. Two
different models are developed, one for joint activity participation and one for
household task allocation, focusing on two-heads households. A classification of
activities is developed and activity types that likely relate to the needs at the household
level are identified. .
Chapter 6 specifies two subsequent models: duration and start-time models for non-
work activities conducted jointly by the male and female head of a household.
Specifically, the study investigates the timing of non-work activities related to
household and family activities, such as household tasks (e.g., escorting persons,
grocery shopping) and non-task activities (i.e., social and leisure activities). The
analysis focuses on two-heads households (with or without children) and the joint
activities in their schedules.
Chapter 7 re-estimates location choice models in the context of household decision
making. It consists of two primary models for respectively independent and joint non-
work activities. As in the basic model, the concept of detour time is used. This concept
considers relative locations to the previous and next activity as the unit of analysis for
defining location choice. By applying that concept, distances between locations that
may be combined in a single trip-chain can be captured well.
Chapter 8 models the car allocation decision to non-work tours. This chapter is similar
to chapter 4 that is concerned with the car allocation decision to work tours.
Nevertheless, the way of defining the car allocation decision is quite different given the
sequential process.
6
Chapter 9 discusses the integration of these sub-models into the integral ALBATROSS
model. It intends to prove that the simulation of sequential choice facets and
observations are little different.
Finally, Chapter 10 summarizes this study, reflects on the results and identifies some
avenues of future research.
REFERENCES
Arentze, T.A. and Timmermans, H.J.P. (2000), ALBATROSS: A Learning-based Transportation Oriented Simulation System. EIRASS, Eindhoven University of
Technology, The Netherlands.
Arentze, T.A. and Timmermans, H.J.P. (2004), A Learning-Based Transportation
Oriented Simulation System, Transportation Research Part B, 38, 613-633.
Arentze, T.A. and Timmermans, H.J.P. (2005), ALBATROSS 2.0: A Learning-based Transportation Oriented Simulation System. EIRASS, Eindhoven University of
Technology, The Netherlands.
Bhat, C.R., Guo, J.Y., Srinivasan, S., and Sivakumar, A. (2004), A Comprehensive
Econometric Microsimulator for Daily Activity-Travel Patterns, Transportation Research Record, 1894, 57-66.
Kitamura, R. and Fujii, S. (1998), Two Computational Process Models of Activity-
Travel Behavior. In: T. Gärling, T. Laitila and K. Westin (eds.), Theoretical Foundations of Travel Choice Modeling, Elsevier, New York, pp. 251-279.
McNally, M.G. (2007), The Four Step Model. In: Hensher, D.A. and K. Button (eds.)
Transport Modeling, 2nd Edition, Pergamon, Oxford, pp. 55-73.
McWethy, B.L., Lemp, D.J., and Kockelman, M.K. (2002), From Aggregate Methods
to Microsimulation: Assessing the Benefits of Microscopic Activity-Based Models
of Travel Demand. In: Proceedings of the 86th Annual Meeting of the Transportation Research Board, Transportation Research Board, National Research
Council, Washington D.C.
Ortuzar, J.deD. and Willumsen, L.G. (1994), Modelling Transport (second edition),
Wiley, Chichester.
7
Chapter 2
LITERATURE REVIEW
2.1 INTRODUCTION
As briefly discussed in the introduction, the activity-based approach to travel demand
forecasting represents an attempt of improving the integrity of demand forecasting
models by explicitly modeling various dependencies. These dependencies are not only
concerned with the various choice facets (generation, destination, transport mode, etc),
but also with dependencies between members of the household. The focus on the
household as opposed to the traditional focus on the individual is especially important
in the context of task and resource allocation and joint activities. Although the
importance of the household level has been recognized in seminal work, except for
some analytical studies, only recently there have been attempts of modeling these
phenomena (Timmermans, 2006).
The purpose of this chapter is to give an overview of this line of research. First, we will
summarize empirical work, followed by recent modeling attempts. Finally, we will
discuss how household decision making is treated in existing comprehensive activity-
based models of transport demand.
8
2.2 ANALYTICAL STUDIES ON HOUSEHOLD DECISION MAKING
2.2.1 Car Allocation and Usage Decisions
Activity participation and destination choice often depend on the transport modes that
are available to individual household members. Car use often means that more
destinations can be visited during a single trip or that destinations further away from
home can be reached within a given time budget. Especially in car-deficient
households in which the number of cars is less than the number of drivers, car
allocation and usage is a household decision which impacts many other choice facets of
individual activity-travel patterns. Golob, Kim, & Ren (1996) analyzed how drivers are
allocated to vehicles in multi-driver/multi-vehicle households. They found that gender,
income, work status, age and the presence of small children influenced the number of
vehicle miles traveled with the various vehicles. Hunt & Petersen (2005) also found
evidence of gender differences.
Almost similar, Vance and Iovanna (2007) also found that gender play a role in
determining the probability of car use and the distance driven. Drawing on a panel data
collected between 1996-2003 in car-owning households in Germany, the results
indicated that although women, on average, perform more non-work travel than men,
they were more reliance on other modes than car. Another interesting study is done by
Vovsha and Petersen (2007). A model system structure is proposed that can fully
address all needs associated with car allocation and use. The core short-term module
includes two long-term sub-models: 1) household car ownership, 2) main driver
assignment for each car, and four short-term sub-models: 3) individual and joint travel
generation, 4) schedule adjustments, 5) mode choice, and 6) car allocation and type
choice. However, neither of the existing model system has yet included a full set in a
consistent way.
2.2.2 Task and Time Allocation Decisions
One would expect that household characteristics, such as the structure and the number
of persons in a household, influence the number and type of activities conducted in the
household and therefore task allocation and travel decisions. Household structure also
influences where (in-home vs. out-of-home) activities are conducted (Gronau, 1977;
Lawson, 1999). A major factor that influences the decision to travel relates to the role
of paid work within a household. The amount of time spent on paid work strongly
influences the budget available for household consumption, and the total amount of
time and the time of day available for other activities. Lee & Hickman (2004), looking
into this, examined time allocation of households within trip chains using simultaneous
9
doubly-censored tobit models. In particular, they compared trip chaining behavior,
among five types of households: single non-worker households, single worker
households, couple non-worker households, couple one-worker households, and couple
two-worker households. They found that household types, defined by the number of
household heads and work status, strongly influence activity time allocation in trip
chains. The presence of children in the household has a positive effect on the duration
of all out-of-home activities in household trip chaining, except for the duration of out-
of-home discretionary activities of households having children under 5 years old. This
suggests that the presence of children induces more chaining of trips and more time
allocated to these trip chains. Households having more children of 16 years of age and
over are more likely to spend time in trip chaining for out-of-home subsistence
activities. Finally, they found that flexible work arrangements tend to be correlated
with less trip chaining for the work trip.
Another consistent finding in the literature is that the work commute of women is
shorter (e.g., Hanson & Hanson, 1980; Hanson & Johnston, 1985; Singell & Lillydahl,
1986; White, 1986; Fagnani, 1987; Gordon, et al., 1989; Hanson & Pratt, 1990; Turner
& Niemeier, 1997). It reflects the fact that on average working women are less flexible
because they need to combine paid work and household activities. Women are able to
combine work and domestic duties primarily by working closer to home, more trip-
chaining and relying on social networks (Hanson & Pratt, 1995: Kwan, 1999; Dowling,
2000). Consequently, accessibility considerations are more important to them, both in
terms of accepting a job, but also because they need to take care of many other non-
work activities. Stopher & Metcalf (1999, 2000) concluded for several cities in the
United States that beyond the effects of lifecycle, both gender and working status
influence the amount of time allocated to household activities (see also Vadarevu &
Stopher, 1996, 1999). Likewise, Schwanen, Ettema & Timmermans (2006) argued that
if a spouse works longer hours, s/he has less time for domestic tasks. Relegating
household activities to one’s partner may then be a reasonable strategy to cope with
this situation. Alternatively, households may consider an overall reduction of
household tasks at the household level (Morris, 1990; Presser, 1994). However, such
effects are gender-specific in the sense that male’s household tasks do not change much
if women work longer hours and women, irrespective of their employment status,
continue to carry prime responsibility for these tasks (Morris, 1990; Pinch & Storey,
1992; Hanson & Pratt, 1995; Presser, 2003). There are nonetheless variations. In
addition to the impact of class, occupation and lifecycle, gender roles and power
differentials among spouses matter (Morris, 1990). Men tend to conduct more
household tasks if spouses’ roles orientations are more egalitarian (Huber & Spitze,
1983; Presser, 1994), and women’s have more resources relative to men (Antill &
Cotton, 1988; Presser, 1994). Yet another strategy may be task specialization. Men
10
may conduct more tasks in larger households with young children to increase the
efficiency of the household or to comply with the prevailing moral climate and gender
ideology (Knijn, 2004).
There is also some evidence that good accessibility stimulates out-of-home activity
participation and trip making (Boarnet & Crane, 2001; Ettema, Schwanen &
Timmermans, 2006). In contrast, poor accessibility, either as the result of the non-
availability of a car or as the result of the spatial distribution of facilities relative to
home may lead households to assign out-of-home household tasks to one spouse –
usually the female – who can combine several tasks in multi-stop activity chains. For
example, Strathman, et al. (1994) concluded that the likelihood of forming complex
commuting chains is higher for women and high-income households, both of which
tend to be “time challenged” groups. If, however, accessibility is better, men may take
on more household tasks, because accommodating such activities in their activity
schedules is easier (Hanson and Hanson, 1980; Ettema, et al., 2006). Kwan (1999,
2000) found that women’s household activities tend to be more fixed in space and time
than those by men, suggesting that such tasks are a structural component of their daily
schedules, while men conduct such activities on a “standby/basis”. This interpretation
is corroborated by Aitken (2000), who concluded from interviews that fathers
responsible for childcare felt they were merely ‘helping out’ their spouses.
Household tasks also have an effect on other in-home and out-of-home activities. For
example, Gronau (1977) looked at the effects of an increase in the number of children
and the age cohorts of the children. He found that as the number of children in a
household increases, the additional time devoted to children is not spent on work at
home and leisure. Similarly, Redman (1980) found that family size had a negative
effect on meals being eaten outside the home. Golob & McNally (1997) used a
structural equation model to investigate activity participation and travel of couples.
Activities were classified into three categories: work, maintenance, and discretionary.
The total out-of-home duration for these categories was calculated as was total travel
time. A series of household and personal characteristics was used as the exogenous
variables of the model. They studied four types of direct effects: travel requirements of
out-of-home activities, within-person activity interactions, within-person travel
interactions, and cross-person interactions. One of the interesting results was that if the
male increases his participation in work activities, the female’s travel for maintenance
activities increases more than proportionally to the increase in the female’s
participation in maintenance activities.
Borgers, Hofman, Timmermans & Ponjé (2001) used a stated choice approach for
estimating the probability of certain task allocation profiles. The main reason for
11
collecting stated choice data was that revealed daily task allocation patterns may be
influenced too much by unique factors. Examining task allocation behavior under
laboratory conditions as a joint decision making process likely generates more valid
data. The problem addressed in this paper was that stated choice experiments typically
involve a choice between single alternatives and not between portfolios (a specific
combination of tasks). The authors therefore explored alternative approaches of how to
measure the influence of experimentally varied factors on task allocation. In a sequel,
Borgers, Hofman & Timmermans (2002) estimated a slightly simpler model. They
assumed that the presence of children of various ages in the household, the socio-
economic status of the household, age, car availability and work status of the spouses
influence time allocation decisions. Multinomial logit models, including these
variables as contextual effects, were used to predict time allocation of two spouses to a
set of activities. First, a multinomial logit model was estimated to predict the amount of
time spent together. Next, a conditional choice model was estimated to predict the
proportion of time spent by each spouse on conducting a particular activity. Because
the total amount of time is known, these proportions can be translated into the number
of hours spent on particular activities. Respondents were requested to jointly express
the amount of time they typically spend alone and together on 27 different activities,
which were later grouped into activity classes. The following activities were
distinguished: (1) sleep, eat, drink and personal care; (2) work out of home, including
travel time; (3) shopping, services, including travel time; (4) in-home non-leisure; (5)
in-home leisure; (6) out-of-home leisure; (7) bring/get activities, and (8) other. Results
indicated that if an older child is present in the household, the amount of time spent
together significantly increases. The amount of time spent together is less if either
spouse works. Time spent on sleeping, eating, drinking and personal care is
significantly less when older children belong to the household. The amount of time
spent on sleeping, eating, drinking and personal care by men is less if their spouses
have a part-time or full-time job. If men have a part-time job, their time allocation to
sleeping, eating, drinking and personal care are higher. In contrast, it is significantly
less if they have a full time job. Men working part-time spend more hours on shopping,
while men working full-time spend less time on shopping. If spouses work, men tend
to shop more, but this effect was less significant.
The effects of the work status variables were interesting. If men work part-time, they
tend to spend more time on in-home work activities, although the effect was not
significant. If they work full-time, they allocate significantly less time to in-home work.
If their spouses work, men also tend to spend more time on in-home work activities,
but this effect is only significant if their spouses work part-time. If their spouses have a
full-time or part-time job, women allocate less time to shopping, which is especially
true if their spouse works part-time. This result might reflect a shift in the overall
12
activity pattern in the sense that they may spend more time together on other activities.
The impact of work status of women is such that working women spend less time on
shopping, but this effect is only significant if they work full-time, and then only at the
90% probability level. Time pressure might be the reason for this finding. The pattern
of the signs of the work status variables is interesting. Compared to the reference
household, time allocation of men to shopping tends to increase if their spouse work,
suggesting that men take over some of the shopping responsibilities of their spouses.
Women tend to spend more time on in-home non-leisure activities if they have young
children and less time if they have older children compared to the situation where there
are no children in the household. This seems to indicate that older children help out.
Similar results were obtained for other categories. Overall, the results suggest that task
and time allocation in households depends on household type (age, children, number of
workers), the utility that is derived from joint versus solo activities, the urgency of
conducting particular activities, gender roles and the constraints and possibilities
offered by the environment to conduct these activities efficiently in time and space.
Ettema & Van der Lippe (2006) investigated task allocation patterns on a weekly basis.
The results of their analyses indicated that specialization is a dominant weekly pattern
in dealing with time constraints, i.e. each spouse takes primary responsibility for
different tasks. The presence of young children and a lower accessibility to jobs and
services increases the female's share of household tasks and childcare. This
specialization is strongest on Friday and on Wednesday, reflecting school hours and
part time work arrangements in the Netherlands. Non-traditional roles and a highly
qualified job increase the females' share of paid labor and decrease their share of
household and childcare tasks, however this effect is not observed on Fridays,
suggesting that women still, more than men, work in part time jobs where Friday is the
free day.
Cao and Chai (2007) examined activity time allocation of the male-female household
head between weekday and weekend. Based on observations on Shenzhen residents in
China, they found the gender role in the household. Men are dominant in out-of-home
activities, but women are more dominant in in-home activities. On average, women
carry more maintenance responsibilities than men, but men spend more time on work
and leisure activities than women, especially on the weekend. On the weekend,
Shenzhen’s residents are not as mobile as westerner countries because most people
spend time at home and surrounding neighborhoods, especially as for female. Further,
the influences of household structure on time allocation of both household heads
demonstrated substantial gender-role differences. The results also showed some
interesting interpersonal interactions of time allocation. Specifically, the more women
13
participate in leisure activities, the more men spend time on leisure activities, but not
vice versa. Although not substantial, women’s work activity duration tends to increase
men’s leisure activities. During the weekday, as women spend more time on
maintenance activities, overall, men as well as women participate in fewer leisure
activities. On the weekend, once one household head works, the other tends to carry
more maintenance activities.
2.2.3 Joint Activity Participation
Joint participation in activities represents a substantial portion of non-work activities, is
an important component of travel during certain time periods and affects individual
travel schedules. Joint participation in maintenance and leisure activities and the
provision of rides to family members, constrain individual choice sets and affect the
saliency of attributes that contribute to the generalized cost of travel alternatives.
Therefore, this choice problem has received relatively most interest.
The relative importance of joint activity participation is evident in that joint activities
tend to have a longer duration than non-work independent activities, and persons tend
to stay out later and travel farther from home (Kostyniuk & Kitamura, 1983).
Moreover, Fujii et al. (1999) found that time spent on activities jointly with other
household members, particularly with children, was incremental to individual feelings
of satisfaction and in decisions to allocate time to joint and independent activities.
Several studies have examined the effect of household attributes on joint activity-travel
behavior. Kostyniuk & Kitamura (1983) and Chandraskharan & Goulias (1999) found
that joint activities involving household heads are significantly affected by the presence
of children. Couples without children living at home are more likely to pursue joint
out-of-home non-work activities than couples with children. In households with
children, most joint activities between adults are at home. In addition, the employment
status of the household heads influences whether a joint activity originated from home
or from an out-of-home contact point.
Another interesting study that investigated the effects of children on household travel
behavior was done by Senbil, et al. (2008). They examined the impact of children on
various household non-commute trips for four different types of non-commute trips,
i.e., trips to shopping, restaurant, park and recreation centers, and department store.
These variables were regressed against socio-economic and demographic,
neighborhood and various child variables by using two regression analyses: linear
regression on household non-commute trips, and seemingly unrelated regression on
14
non-commute trips by male and female household heads. Results for linear regression
suggest that children can be grouped under general groups, i.e, pre-school, school, for
accounting child effect on household travel behavior. Besides, number of children
reveals significant results. Also in linear regression analyses, they found that the
differentiation outperforms a classical lifecycle classification. Results for seemingly
unrelated regression suggest that there is a general complementary among household
heads in non-commute trips, except shopping trips which display substitution, albeit
minor. Also, for household heads, pre-school children constitute the child group with
significant effects on non-commute trips.
Srinivasan and Bhat (2008) examined the joint participation with household members
and non-household members along with the generation, location, and scheduling of
joint activity episodes. They found that independent activities are different from joint
activities in systematic ways. Specifically, joint episodes are of longer durations,
significantly likely to take place at the residence of other people, and often confined to
certain time periods of the weekday. In addition, within the class of joint episodes,
important differences are also observed based on activity type, companion type, and
the day of the week. Overall, the empirical results from this study highlight the
important need to accommodate intra-household and inter-household interactions in
activity-travel behavior analysis. Specifically, some of the key implications of their
empirical findings include the following. First, given the sheer magnitudes of joint
activity and travel engagement, their results underscore the need for travel demand
models to recognize these inter-dependencies for accurate travel forecasts and policy
analysis. In particular, inter-personal linkages in activity travel behavior imply that
policy actions can also alter the travel patterns of individuals who are not directly
“exposed” to the action. For example, when a husband’s work timings change because
of work-staggering, the wife’s travel patterns can also change. Second, the timing (i.e., duration and time-of-day) of activity-travel is found to be related to the companion
type. Consequently, accurate assessment of soak time distributions for air quality
models requires information on joint activity-travel engagement patterns. Third, a high
fraction of joint leisure type activities is found to be undertaken at “someone else’s
home”. The implication here is that individuals are perhaps not as flexible in their
choice of destination location for the pursuit of discretionary-type activities as they
have been traditionally assumed in travel-demand modeling. Fourth, the desire to
participate in activities with non household members such as friends also generates
additional travel to pick-up and drop-off the activity companions. Such travel cannot be
realistically captured by individual-level models. Fifth, with the gaining prominence of
the need to model weekend travel behavior, accommodating inter-personal interactions
assumes even greater significance as joint activity and travel participation levels during
weekends are found to be greater than those during weekdays. Finally, to enable the
15
development of empirical models that accommodate inter-personal interdependencies,
future travel surveys should be suitably enhanced to adopt a more disaggregate activity
classification scheme and to collect data on individuals’ activity and travel companions.
2.3 PARTIAL MODELS OF HOUSEHOLD DECISION MAKING
2.3.1 Car Allocation
Petersen & Vovsha (2005, 2006), in addition to car allocation, also modeled car-type
choice. First, they simulated which individual and joint activities are conducted and
where these activities are conducted. Then, accessibility to the most important
activities (work and school) in combination with the household characteristics
determines car ownership by vehicle type. Next, generated activities are scheduled and
out-of-home activities are distributed by travel tours. Travel needs of the household
members are further consolidated through joint travel arrangements. Finally, available
household cars are allocated to these tours. The authors argue that numerous feedbacks
can be implemented within this framework in order to enhance the integrity of the
model system and eliminate possible inconsistencies. Interestingly, they notice that
only some of them can be formalized as log-sums in a nested logit model. Other
feedbacks are more complicated in nature and require rule-based algorithms. For
example, re-scheduling and tour-formation procedures are needed to synchronize tours
and enforce joint travel arrangements. If the total time budget proved to be unrealistic
in terms of the travel time share, adjustment of certain activities and locations is
needed.
The actual model is a multinomial model which predicts the choice of household car. A
maximum of 8 choice alternatives is distinguished, varying in terms of five car types
(small auto with 4 or less cylinders; large auto with 6 or more cylinders; van;
SUV/jeep; truck, and car age in years). If a household has less than 8 cars, unavailable
choice alternatives are blocked out. For each tour, assumed known are tour-related
attributes (purpose, destination, distance, schedule, number of stops, pure auto tour
versus drive-to-transit tour), driver-related attributes (person type, gender, age), joint-
travel-related attributes (party type, party size, fully joint versus partially joint tours),
household-related attributes (income group), and zonal attributes (area type at the
origin, area type at the destination). Purpose, distance, number of stops, driver type,
party type, joint activity participation and socio-demographics were used as
explanatory variables.
As part of the TASHA model system, Miller, Roorda & Carrasco (2003) developed a
tour-based model of travel mode choice, based on the principle of utility-maximization.
16
In particular, cars are allocated to household members such as to maximize household
utility, which is assumed to be the sum of household members’ individual utilities. The
scope of explanatory variables is largely restricted to travel time and costs of
alternative transport modes and trip purpose.
2.3.2 Task Allocation
Wen & Koppelman (1999, 2000) proposed a prototype activity stop generation and
tour scheduling model that includes the daily allocation of household maintenance
tasks and automobile use. Their model focuses on travel that is generated from
participation in activities undertaken to satisfy needs and desires of the household and
its members. The model itself is a nested logit model that differentiates between
household subsistence (work and work-related business) needs and mobility decisions,
the generation of maintenance (grocery shopping, personal and household business)
activities (stops) which serve the household in general and each member of the
household and the allocation of stops and autos among household members exclusively
or jointly. Finally, individual daily travel/activity patterns are derived through the
generation of tours, the assignment of stops to tours, and the selection of locations for
each stop and travel mode(s) for tours. The highest level is the choice of the number of
household maintenance stops. The second level is the allocation of maintenance stops
to individuals. The lowest level of the model concerns the allocation of cars to
individual household members. The second stage choices, for each adult household
member, include the number of tours and the assignment of stops to tours, conditional
on the choices of the number of maintenance stops and the allocation of stops and
autos. A distinction is made between workers and non-workers. Thus, this model
remains restrictive, both in terms of characterization of activity patterns level
characteristics and the limited choice facets that are included in the model. Time
allocation and the utility derived from different types of activities are not included in
the model, although it is an important consideration in leisure and maintenance
activities and an essential criterion for decisions regarding joint activity participation.
Zhang, Timmermans & Borgers (2002) developed a more general model of task
allocation and time use of household members. They assumed that households allocate
their time to activities such that household utility is maximized. In contrast to many
other models, household utility is not assumed to be a simple sum of household
members’ utilities, but also incorporates relative influence and interest. Starting point is
the assumption that every household has to perform a set of activities to survive or to
give some meaning or pleasure to their daily life. The utility of these activities is
assumed to differ between individuals. Role patterns within households and more
17
general lifestyle decisions influence the kind of activities that are conducted, the
household member primarily responsible for the task, and activity participation and
allocation of time across activities (and related travel). Activities are classified into
four types, i.e., in-home activities, out-of-home independent, allocated and shared (or
joint) activities. An independent activity is an activity, not being a household task that
is conducted by an individual household member (e.g., work or attending a football
match). Shared activities are those activities that require the presence of more or all
household members (e.g., dinner or a family outing). An allocated activity is a
household task that is assigned to a specific household member (e.g., daily shopping).
Shared activities may be synchronized or non-synchronized. In the former case,
household members carry out the activity together. In the latter case, household
members share the activity partially. The basic structure of their model was formulated
as:
Maximize )u,...,u,u(GGUF n21= [2.1]
Subject to ij ij Tt =∑ , for i = 1, 2, …, n [2.2]
where,
GUF stands for group (household) utility function,
ijt is the time of individual i performing activity j,
iu is household member i’s utility, and
iT is member i’s available time.
A set of alternative specifications of the group utility function was considered. The
multi-linear group utility function can be specified as follows:
n21n~1
n
1i ii iiii
n
1i ii ...uuuw...)uu(wuwGUF1 12 2121
+++= ∑ ∑∑ = >= [2.3]
where,
iw is member i’s weight parameter, and
n~1ii w,...,w21
are the intra-household interaction parameters.
This model assumes that household utility can be derived by weighting the utilities of
the individual household members, and adding interaction effects. The weight wi can
be interpreted as a measure of a member's power or influence in the group decision-
making. The interaction parameters { n~1ii w,...,w21
} moderate the power effect and
reflect the group members’ concern for achieving equality of utilities. The larger the
18
interaction parameter, the higher the group’s collective desire to choose a time
allocation such that the utilities of all household members are more or less equal.
The GUF in equation (3) finds its theoretical roots in group decision theory (Eliashberg
& Winkler, 1981, 1986; Harsanyi, 1955; Keeney, 1972; Messer & Emery, 1980). It can
include several GUFs as special cases. The additive-type group utility function only
uses the first component of the utility function and can be expressed as:
∑ ==
n
1i iiuwGUF [2.4]
Harsanyi (1955) showed that if the group is to behave in a Bayesian rational manner,
then the group utility function must be additive. This model can be arrived at when
household members first average their separate utility functions and then maximize the
resulting mixture function (Curry, et al., 1991). However, this GUF ignores the
interaction among household members. An alternative is a compromise-type group utility function which can be expressed as:
∑∑ ====
n
1i i
n
1i ii /n)(uuwGUF [2.5]
Equation [2.5] shows that household members have equal weights. Curry & Menasco
(1979) called this the compromise weight. There is some empirical support in other
disciplines for such equal weighting (e.g., Davis & Rigeaux, 1974; Munsinger, Weber
& Hansen, 1975; Krishnamurthi, 1988), but there is also empirical support of non-
equal weights (Molin, Oppewal & Timmermans, 1997, 2000). Hence, it may advisable
not to assume equal weights a priori.
Another special case is the capitulation-type group utility function, which takes group
interaction into account by assuming that each household member uses other members’
weights (utilities) as his or her own weight (utility) for joint decision-making.
∑ ==
n
1i iiuwGUF or ∑ ==
n
1i iiuwGUF [2.6]
where iw represents the average weight of other members relative to member i and is
called capitulation weight, and iu represents the average utility of other members
relative to member i and is called capitulation utility.
An alternative to these linear functions are Nash-type functions of a multiplicative
form. The group utility function can be expressed as:
19
( ) iw
i iuGUF ∏=
[2.7]
Equation [2.7] shows that this utility function is multiplicative without a reference
point. It satisfies Nash’s (1950, 1953) axioms for two-party cooperative games or
variations on those axioms. The Nash model assumes that each household member
identifies his/her most preferred outcome and the household then compromises by
averaging along the resulting negotiation frontier (Curry et al., 1991). Gupta & Livne
(1988), however, pointed out that Nash’s definition was particularly inappropriate for
multiple-issue bargaining and suggested the following definition.
( ) iw
i ii uuGUF ∏ −=
[2.8]
This type of GUF uses other members’ capitulation utility as a reference point. Curry et al. (1991) have experimentally tested the validity of this utility function. The reference
point suggests that during negotiations each member can be expected, explicitly or
implicitly, to compare each possible agreement against the reference point.
Zhang, et al. (2002) decided to use the multi-linear specification because it is easier to
estimate and it is more general. Their model only incorporated binary interaction terms
rather than multiple interaction terms. Thus, the estimated model can be formulated as
follows:
∑ ∑∑ = >=+=
n
1i ii iiii
n
1i ii1 12 2121
)uu(wuwGUF [2.9]
Each member’s utility function is further composed of the utilities from different
activities based on the same type of multi-linear GUF.
∑ ∑∑ >+=
i ii iiiii ii a1 a1a2 a2a1a2a1a aai )uu(ruru [2.10]
where,
iau is household member i’s utility for activity ia ,
iar is member i’s weight (or relative interest) for activity ia , which reflects the relative
importance of each activity making for each member’s utility, and
ii a2a1r is interaction parameter for activities i1a and i2a .
20
In particular, they proposed the following utility maximization framework to model
household time allocation based on the multi-linear household utility function, subject
to each member’s available time constraint.
Maximize ∑ ∑∑ +=+=
i 1ii' i'iii'i ii )uu(w)u(wGUF [2.11]
shri
alci
asi
shri
indi
isi
alci
indi
iai
shri
homi
hsi
alci
homi
hai
indi
homi
hii
shri
shri
alci
alci
indi
indi
homi
homii
uuruuruur
uuruuruur
ururururu
+++
+++
+++=
[2.12]
Subject to
ishri
alci
indi
homi Ttttt =+++ [2.13]
ii',ttt shrshri'
shri ≠∀== [2.14]
where, homit is the time staying at home, indit , alc
it and shrit are the time of member i performing out-of-home independent activity
(ind), allocated activity (alc) and shared activity (shr), respectively,
iT is member i’s available time for performing all these activities, shri
alci
indi
homi uu,u,u and are the utility functions,
shri
alci
indi
homi rr,r,r and are their weight parameters, and hi
ir , hair , hs
ir , iair , is
ir and asir are the interaction parameters.
In order to derive operational models of household time use, the following type of
utility function for each activity was used.
( ) ( ) }alc,ind{hom,j,xexp1tlnu jiq
jiq
jiq
ji
ji =++= ∑ εβ
[2.15]
( ) ( )shriq
shriq
shriq
shrshri xexp1tlnu εβ ++= ∑ [2.16]
where, shri
ji and εε are error terms of utility functions, j
iqx , shriqx are explanatory variables
and, jiqβ and shr
iqβ are the parameters.
21
A La Grange function was used to calculate the maximum of the household utility,
subject to the constraints of equation [2.13] and [2.14]. By calculating the conditions
for the first partial derivatives with respect to the time of each activity ( homit , ind
it , alcit
and shrit ), the models for household time use for respectively in-home activity, out-of-
home independent, allocated and shared activities were derived. A seemingly unrelated
regression (SUR) estimation procedure was applied to estimate the models.
The model was initially estimated for 188 households, who reported their activity-
travel patterns in the South-Rotterdam region, the Netherlands. The following
explanatory variables were used: socio-economic class, age of the oldest household
member, household type (no worker, single worker and double worker), the number of
owned cars and bikes, and official work hours per week) for each household member.
The goodness-of-fit of the model was satisfactory, but should be improved. Another
result of the estimation was that the influence of the male on time allocation was on
average higher than the influence of the females in the sample.
In a sequel, Zhang, et al. (2005a) also included travel time in the model. This improved
model performance significantly. Furthermore, the husband has the highest influence in
the allocation of time in nearly half the households; in one-fifth the wife had more
influence, while the remaining households showed evidence of equal relative influence.
They also compared weekday versus weekend time allocation (Zhang, et al. (2005b),
and concluded that, the wife behaves more rationally than the husband on weekdays,
while the husband does so on weekends; that the influence of intra-household
interaction and interdependency among activities seems invariant across days of the
week; that number of owned passenger cars influences the couples’ task and time
allocation behavior on weekends, not on weekdays, and that shared activities become
much more important on weekends than on weekdays.
Later, Zhang, et al. (2004) extended their basic model to also include dependencies
among activities. The results suggested that decisions regarding household task and
time allocation start with in-home activities of household members and personal and
joint out-of-home activities, after which the allocation of time to allocated activities is
negotiated. Women seem to regard the allocated activities more important and the in-
home activity less important than men do.
Zhang & Fujiwara (2004) also estimated an iso-elastic household utility function of the
following form:
1wand0w,uw1
1HUF
i iii ii1 =≥
−= ∑∑ − α
α [2.17]
22
where α is parameter indicating intra-household interaction.
The iso-elastic function is known from research on social welfare (Atkinson, 1970).
The intra-household interaction parameter α describes how and to what extent the
household positions its members (or considers the existence of its members) in the
decision-making process. Therefore, different values of α and iw , and the sign of α
represent different household decision-making mechanisms. Equation [2.17] includes
various types of household utility functions as special cases. For example, a minimum type household utility function is obtained if α is larger than one. In this case,
increasing the utility of the weaker member in a household leads to an increase of total
household utility. In case α becomes positive infinity, the household regards the
utility of its weakest member as the household utility and maximizes it, as shown
below.
( )n,...,2,1i|uminGUF i == [2.18]
If α approximates one, equation [2.17] becomes a “Nash-type” household utility in
the sense that each member first identifies his/her most preferred outcome of household
decision-making and the household then compromises by averaging along the resulting
negotiation frontier (Curry, et al., 1991).
( ) iw
i iuGUF ∏= [2.19]
If α is equal to 0, equation [2.17] results in a “utilitarianism-type” of household
utility, which assumes that the household first averages its members’ separate utilities
and then maximizes the resulting mixture utility function. Finally, if α is negative, the
household utility increases with the utilities of the strong household members. In case
α reaches negative infinity, equation represents “maximum” type of household utility
function. That is to say, the household regards the utility of its strongest member as the
household utility.
( )n,...,2,1i|umaxGUF i == [2.20]
Zhang, et al. (2005) compared these alternative utility functions and found the multi-
linear household utility function to have a better goodness-of-fit than the iso-elastic
function for data, pertaining to Japan.
23
2.3.3 Joint Activity Participation
Research into joint decision structures is less prevalent, although it should be realized
that some models of time use include joint activity participation (see previous section).
Fujii, et al. (1999) modeled the allocation of an individual decision-maker’s time to in-
home and out-of-home activities with other family members, with non-family members
and alone, using a production function paradigm. Gliebe & Koppelman (2001) argued
that at the time of their writing no researcher has presented a model of household
decision making in which the utility of multiple decision makers is represented in both
an individual and a collective sense for the purpose of explaining joint activity
participation and travel. They assumed that the joint decision is an aggregation of
individually formed preferences and that households make activity decisions to
maximize collective utility, subject to time constraints. Individual utility is weighted by
the importance of that person to the household’s total utility. Individual utility is
assumed to be a monotonically increasing function of four components: (1)
consumption of the products of market work (subsistence activity) and household
maintenance activities; (2) satisfaction derived from participation in market work,
household maintenance and leisure activities; (3) altruism from the utilities of other
household members; and (4) companionship from participation in maintenance and
leisure activities with other household members. Overall, different explanatory
variables play different roles in the utilities of different activities for different members
in the sense that they have different values of estimated parameters and statistical
significance.
Scott & Kanaroglou (2002) developed a trivariate ordered probit model to model the
daily number of non-work, out-of-home activity episodes for household heads,
accounting for two activity settings: independent and joint activities. The differentiated
between different types of households: couple, non-workers; couple, one worker, and
couple, two workers. Significant interactions between household heads were found, the
nature of which varied by household type. Traditional gender roles were found to
persist in couple, one-worker households. In terms of predictive ability, the models
incorporating interactions were found to predict more accurately than models
excluding interaction.
Meka, Pendyala & Kumara (2002) examined interactions between two adult household
members in multi-adult households using a data set derived from a 1999 household
travel survey conducted in Southeast Florida. Daily activity and time allocations
between two household members were examined and potential trade-offs and
complementary effects were modeled simultaneously using a structural equations
modeling methodology. In particular, their focus was on causal relationships among
work and non-work activity and travel durations and frequencies. The model included
24
six endogenous variables with six significant error covariances and captured within-
person trade-offs between work and non-work activity engagement. For each person, as
the amount of work activity or travel increased, the amount of non-work activity or
travel decreased. Between persons, the model captured the complementary and joint
nature of non-work activity engagement where household members tend to pursue non-
work activities together. Thus, when one person’s non-work activity or travel
increases, so does the other person’s non-work activity travel engagement.
Srinivasan & Bhat (2006) simultaneously modeled: (1) the male’s decision to
undertake independent in-home discretionary activities and the corresponding duration,
(2) the female’s decision to undertake independent in-home discretionary activities and
the corresponding duration, (3) the male’s decision to undertake independent out-of-
home discretionary activities and the corresponding duration, (4) the female’s decision
to undertake independent out-of-home discretionary activities and the corresponding
duration, and (5) the household’s decision to undertake joint out-of-home discretionary
activities and the corresponding duration. The discrete components of the choices (i.e., the decision to undertake activity) are each modeled using the binary logit structure.
The continuous components of the choices (i.e., the activity duration) are each modeled
using a linear regression structure with the natural logarithm of the corresponding
activity duration as the choice variable.
Srinivasan & Bhat (2008) also considers joint participation accommodating intra-
household and inter-household interactions in activity-travel behavior analysis, and
examines the generation, location, and scheduling of joint activity episodes. The results
of this analysis highlight the high levels of joint activity- travel participation by
individuals. Further, independent activities are found to be different from joint
activities in systematic ways. Specifically, joint episodes are of longer durations,
significantly likely to take place at the residence of other people, and often confined to
certain time periods of the weekday. In addition, within the class of joint episodes,
important differences are also observed based on activity type, companion type, and
the day of the week.
2.3.4 Travel Arrangements
Vovsha, et al. (2005) conceptualized a ride-sharing for mandatory activities as a pure
travel arrangement, where the underlying activity for each participant is assumed to
vary between individuals. Thus, they argued that different from joint activity ride-
sharing modeling for mandatory activities does not require a generation model but
rather a linking and synchronizing model. This is a limiting conceptualization in that it
25
implicitly assumes that activities are fixed. When modeling ride-sharing, the authors
assumed that for each household member the number and purpose of mandatory tours
and their location zone, preferred departure from home, and preferred arrival back
home are known for each tour. They differentiate between outbound and inbound ride-
sharing. Their model involves two stages (i) linkage and synchronization of outbound
and inbound half-tours by means of a partition-choice model that considers all possible
partitions of mandatory half-tours into rides (alone and shared); (ii) Ordered
participation choice model that essentially considers a role of each participant (driver,
passenger) and route along which activity locations of all ride participants are visited.
To restrict the number of possible linkages thresholds, including maximum allowable
differences in departure/arrival times and maximum deviation from the shortest path to
or from the location of activity for the driver are assumed. In addition, the maximum
size of travel party was limited to 3 participants.
The person participation role model considers sequences of persons within the ride in
such a way that the first person plays the driver role, the second person corresponds to
the passenger with the longest route, and so forth. The last person is the first passenger
dropped off on the outbound half-tour or the last person picked-up on the inbound half-
tour. The last person does not experience any route deviation. The order of persons
from the driver to the shortest-leg passenger corresponds to the magnitude of potential
deviations from the shortest route.
Another interesting and in some respects more elaborate model was suggested by
Roorda, Miller & Kruchten (2006). The differentiate between joint trips, serve
passenger trip (see also next section), pure joint tours, partial joint tours, pure serve
passenger tours, and en-route serve passenger tour. The model incorporates individual
tour mode choice, vehicle allocation, a serve passenger matching procedure, and pure
serve passenger tours, and optimizes a utility function. In their application, the number
of explanatory variables was rather limited, but in principle this could be extended to
encompass a wider selection of personal, household, transportation, and especially
activity-travel pattern characteristics.
Escorting is a joint travel arrangement that is characterized by different roles of
participants. There is always an escorting adult driver and one or several escorted
children. Vovsha, et al. (2005) argue that the important characteristic that distinguishes
escorting from all other joint activity and travel arrangements is that only the escorted
persons have a purposed activity to participate while the driver does not participate in
any activity and implement a pure chauffeuring function. A dominant share of
escorting involves children as passengers. For each tour of a child that demands
escorting they distinguish five possible alternatives: 1) no escort; 2) escort in outbound
26
direction only (from home to activity), 3) escort in inbound direction only (from
activity back home), 4) escort in both direction by means of two separate tours of the
same driver or by different drivers without waiting, and 5) escort in both direction by
means of a single tour of the same driver with waiting. The set of children’s tours with
all pertinent characteristics of the person tour purpose/activity type, departure-from-
home time for outbound half-tour, arrival-back-home time for inbound half-tour, and
location is assumed known and fixed. The set of adult chauffeurs with all pertinent
characteristics of the person and availability to serve child tours within the time
window left after scheduling the chauffeur’s mandatory and joint activities (they are
considered of higher scheduling priority) is also assumed known and fixed. Escorting
tours for each chauffer are listed in a chronological order. The first escort tour can take
any outbound or inbound child half-tours that fall into the available time window of the
chauffeur, while each subsequent escorting tour of the same chauffeur has a narrower
window available since the previous tour(s) blocked out some time. Three feasible
conditions are adhered to: The bundle of outbound half tours of children served by the
tour should have close departure-from-home times and locations. A threshold was used
for bundling outbound half-tours. The bundle of inbound half tours of children served
by the tour should have close arrival-back-home times and locations, and all outbound
half tours start earlier than inbound half-tours served by the same escorting tour. These
constraints normally reduce the choice set size significantly. However, further
decomposition may be required, for example by ordering household chauffeurs. Then,
the choice model is developed for a single person and includes only residual
chauffeuring alternatives left after the choices actually made by the previously modeled
chauffeurs. Alternatively, Vovsha & Petersen (2005) suggest an ordering of child tours
demanding escort rather than an ordering of chauffeurs. The utility function then
consists of some combination of escorting utility for each child half tour (no
escort has zero utility), additional child utility of escorting in both directions, chauffeur
suitability and availability for each child half-tour, chauffeur workload saturation effect,
and chauffeur tour disutility.
2.4 HOUSEHOLD DECISION MAKING IN COMPREHENSIVE ACTIVITY-
BASED MODELS
In this section, the existing comprehensive activity-based models will be reviewed in
terms of their inclusion and treatment of household decisions. Comprehensive in this
context means that the model allows predicting a combination of choice facets, at least
compatible with those underlying traditional four-step models: i.e. activity generation,
destination and transport mode choice. Also, we restrict our discussion to fully
operational models.
27
Over the years, many activity-based models have been suggested in the literature,
including constraints-based models, micro-simulation models, (nested logit) utility-
maximizing models, suites of advanced statistical models and rule-based models (see
Timmermans, Arentze & Joh, 2002 for a recent more detailed overview).
2.4.1 Constraints-based Models
These models have primarily been developed to assess whether a planned activity
schedule is feasible, given a set of institutional and space-time constraints. These
models have a long history in activity-based analysis from the early work of
Hägerstrand and his co-workers (PESASP, Lenntrop, 1976) to more recent models
such as MASTIC (Dijst & Vidacovic, 1997) and GISICAS (Kwan, 1994, 1997).
Although there are subtle differences between these models, all have individual activity
schedules as input. Moreover, their purpose is primarily to assess accessibility
conditions and the feasibility of activity schedules as opposed to predicting activity-
travel patterns. Hence, to the best of our knowledge, these constraints-based models
have not dealt with household decision making. However, at least theoretically, it
seems straightforward to extent these models to the household level and assess the
feasibility of household activity schedules, incorporating synchronizing constraints,
possible task allocation and resource allocation. A computational problem is the
explosion of possible combinations of patterns that need to be evaluated. If the purpose
of the model is to identify the number of feasible household activity-travel patterns, a
sophisticated algorithm will be required. If the purpose is to generate a single feasible
activity-travel pattern, even a simple genetic algorithm will be sufficient (e.g.,
Charypar & Nagel, 2004; Meister, Frick & Axhausen, 2005, although it did not (yet)
account for all types of constraints typically incorporated in constraints-based models).
2.4.2 Simulation Models
Examples of these models are McNally (1997) and Ramblas (Veldhuisen, Timmermans
& Kapoen, 2000, 2005). Pribyl & Goulias (2005) are the only ones to my knowledge
who suggested an approach to simulate activity patterns that take interactions within
the family into account. Their approach consists of 6 steps. The objective of step 1 is to
find groups of households with similar activity patterns. To that effect, a K-medoid clustering is applied to the activity patterns of the household, combining the patterns of
the adults. Next, in Step 2 the probability that an individual starts a particular activity at
a particular time and its duration are derived. For every cluster and every time step, the
relative frequencies of leaving home to conduct a particular activity are derived. In
addition, for each time step, activity type and means of travel, the average duration and
28
standard deviation of duration are computed. Then, in Step 3, the identified clusters are
linked to the persons in the data set, based on their personal socio-demographic
characteristics as well as characteristics of their entire household, using a CHAID-
based decision-tree algorithm. In Step 4, the decision trees are used to assigns
households to a cluster. Following Arentze & Timmermans (2003), a probabilistic
action-assignment rule is used. Once households have been assigned to clusters, step 5
simulates daily activity patterns. The activity patterns consist of the sequence of
activities, each with their start time, duration, and the within household interactions.
The model is constructed for each time step from the proportion of cluster members
that start each particular activity within half hour on either side of the time step in
question. Travel is not treated as a separate activity, but rather as an indivisible part of
each activity. A normally distributed random number with the mean value and standard
deviation obtained from the sample for a particular activity type is used at every time
instant.
Another important issue that needs to be addressed is the simulation of alone or joint
activity participation in multi-adult households. The patterns of all adult household
members are simulated sequentially. First, the pattern of the first person in the
household (head of the household) is simulated. In case an activity is a joint activity
with the spouse, the schedule of the spouse defines an exact part of her/his schedule.
The remainder of the schedule is simulated, conditional on the derived probabilities
and the joint parts of the schedule. The probability of an activity to start at the end of
the joint activity is used.
This approach can be viewed as an effective and straightforward extension of the
simulation models that we have in mind. A potential problem of these approaches,
however, is that the simulated schedules may be infeasible within a specific spatial-
temporal context, and this problem may be exaggerated for household activity
schedules. Because these simulation models use observed data, they lack the behavioral
mechanisms of how individuals and household adjust their preferred schedules in time
and space to cope with the various types of constraints they face.
These problems may be more profound from repeated sampling from distribution, one
of the reasons why Veldhuisen, et al. (2000) sampled complete activity-travel patterns.
Alternatively, one can extract skeleton (e.g., Janssens, Wets, Brijs & Vanhoof, 2005),
but such an approach should be extended to cope with possible inconsistencies between
simulated patterns and space-time constraints. This may not be a major issue in similar
(planned) spatial contexts, but too strong an assumption if the space-time
characteristics dramatically differ. Under such circumstances, one cannot reasonably
expect that similar activity-travel patterns will or can be implemented.
29
2.4.3 Utility-Maximizing Models
Over the years, several activity-based models, founded on the principle of utility
maximization, have been suggested in the literature. Most of these have relied on
nested or GEV logit models (e.g., Kawakami and Isobe, 1982, 1989; Bowman, 1998;
Fosgerau, 1998; Wen & Koppelman, 1999; Bowman & Ben-Akiva, 2001).This
approach has been further developed for applications in the United States. An overview
of this work is given in Vovsha, Bradley & Bowman (2005). This work, however,
constitutes an exception in the sense that all other models are based on individual
patterns.
In particular, they consider three principal layers of intra-household interactions: (i)
Coordinated principal activity pattern (DAP) types at the entire-day level; (ii) Episodic
joint activity and travel and (iii) Intra-household allocation of maintenance activities.
DAP’s are coordinated to make sure that a particular household member can, for
example, take care of the children at home. They distinguish between a mandatory
pattern, further subdivided into work, university and school, and the frequency of tours,
a non-mandatory pattern and a stay home pattern. Vovsha, Peterson & Donnelly
(2004a) and Bradley & Vovsha (2005) showed a strong correlation between DAP types
of different household members. Joint activity and travel is distinguished between fully
joint travel tours for shared activities and partially joint tours, in which household
members share transportation without participation in the same activity. Finally, intra-
household allocation of maintenance activities is relevant because the allocation of
such activities to a particular household member is a function of a household decision-
making process. The following categories of out-of-home episodic joint activity and
travel are distinguished: Joint travel generated by the shared activity; Joint travel to
synchronize mandatory activities, and Escorting. This leads to a sequence of five
models: 1-coordinated DAP, 2-joint travel for shared non-mandatory activity, 3-joint
travel (ride-sharing) for mandatory activities, 4-escorting children, and 5-allocation of
maintenance tasks. Alternative DAP types are broken down into a group, containing
individual mandatory activities and a group containing non-mandatory and staying at
home patterns that potentially can be conducted jointly by several household members.
From a modeling perspective, the authors have attempted different structures. First,
they adopted a sequential processing of persons according to an intra-household
hierarchy in several regional travel models in US (Vovsha, et al., 2004a). Second,
simultaneous modeling of potentially joint alternatives for all household members with
subsequent modeling of individual alternatives can be attempted. This involves for
each household member a trinary choice (M, NM, H) and modelling sub-choice of the
mandatory alternative by a separate choice model conditional upon the choice of
mandatory alternative in the trinary choice (Bradley & Vovsha, 2005). Finally, a
30
parallel choice structure that considers combinations of main trinary choices at the
upper level and individual sub-choices simultaneously in one choice structure can be
applied (Gliebe, 2004; Gliebe & Koppelman, 2004, 2005). These nests correspond to
the combination of activities where joint participation is essential. The structure of
these nests captures different levels of intra-household interaction. Under each nest, the
correspondent individual choices of mandatory alternatives are considered for each
person individually.
Episodic joint non-mandatory activities are associated with fully-joint travel tours. A
frequency-choice model is used to predict the number of joint tours by purpose /
activity type at the household level. Subsequently, a Person participation choice model
predicts probability of having a certain participation matrix conditional upon the
chosen set of joint tours (Vovsha, Person & Donelly, 2003, 2004). The various
structures are modeled by a simple nested logit model or generalized nested structures
of the GEV class.
Allocation of maintenance tasks to individual household members is modeled as a two
step process. First, a frequency choice model predicts the number of maintenance tasks.
Next, each task is assigned to a particular household member. Vovsha, Peterson &
Donnelly (2004b) applied a task allocation choice model that is applied for each task
independently and returns a choice probability of a person the most suitable for the task
as a function of the activity type and person characteristics (person type, residual time
window left after mandatory activities, the number of joint and escorting tours in which
the person participates, etc). Next the resulting fractional matrix of allocation
probabilities obtained at the previous stage is discretionized instead of a simple Monte-
Carlo pick for each row because independent picks for each task may result in illogical
allocations with one person overloaded while others may have no tasks.
2.4.4 FAMOS
FAMOS (Pendyala, et al. 2005), derived from HAGS/PCATS developed by Kitamura
and his colleagues in Japan (e.g., Kitamura & Fujii, 1998), is a micro-simulator of
individual-level activity-travel patterns. The model system does not include explicit
household-level allocation models, but the individual-level models do incorporate
“intra-household interaction” effects. The individual activity type choice models, for
example, incorporate variables reflecting household demographics and associated
activity needs. The individual mode choice models consider household vehicle
availability and the micro-simulator keeps track of the availability or non-availability
of household vehicles at any point in time. There is no explicit model of joint activity
31
engagement; however, household level activity-travel patterns (including joint travel)
could be constructed/deduced from the simulated individual-level activity-travel
patterns.
2.4.5 CEMDAP and CEMDAP-2
This model system, developed by Bhat and his co-workers (2004), can best be viewed
as a suite of separate models, predicting the activity travel patterns of workers and non-
workers, and students and non-students. In turn, for some segments, the patterns are
further broken down into sub-patterns. For example, the daily pattern of workers is
characterized by four different sub-patterns: before-work pattern, commute pattern,
work-based pattern, and after-work pattern. Within each before-work, work-based and
after-work patterns, there might be several tours.
Considering practical implementation constraints, certain restrictions are imposed on
the maximum number of tours and the maximum number of stops in any tour. A set of
22 different advanced econometric models is used to predict different facets of activity-
travel patterns, where the type of model chosen does justice to the statistical properties
of the data. This is a strong feature of this model system. On the other hand, where one
of the major objectives of developing activity-based models was improved integrity
and better capturing the many interdependencies in activity-travel patterns, the
CEMDAP models does not differ that much in a fundamental sense from traditional
four step models. First, activity generation is predicted, much as trip generation used to
be modeled. The explanatory variables are largely socio-demographics data:
constraints and characteristics of the daily patterns do not play a major role: pattern
level characteristics are limited in number of level of detail. In terms of household
decisions, the model system is primarily based on individual choices. Household
characteristics sometimes are used as explanatory variables, but processes such as
coordination, synchronization, etc are not explicitly represented.
The new version of CEMDAP considers joint activities, though data constraints did not
allow considering all possible joint activities among each subset combination of
individuals in the household. This is seen as a separate choice and hence, the utility of
joint activities against individual activities is not addressed in much detail. Car
allocation (as needed) and task allocation are also modeled as part of CEMDAP.
Children's activity-travel behavior is explicitly modeled in CEMDAP now, which is a
distinct feature.
32
2.5 CONCLUSIONS
This chapter described several studies applications in household decision making.
Incorporating household decision-making into activity-based models of transport
demand receives increasing attention in recent times. The out-of-home activity needs
synchronization between persons in particular in multi-person households. Problems
such as activity allocation, joint activity participation and resource allocation have in
the past typically been addressed separately, i.e. not in the context of a scheduling
process or at most ad hoc as part of a more comprehensive activity-based model of
transport demand. Decisions to travel jointly in multi-person households require joint
decision between persons.
The comprehensive operational system of activity-based travel demand modeling is
still few exist until recently. ALBATROSS concerns about joint decision making
matter. Therefore, the development of ALBATROSS could enhance the studies of
activity-scheduling models in activity-based transport demand modeling.
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Chapter 3
FRAMEWORK
3.1 INTRODUCTION
Over the last decades, it has become increasingly more recognized that travel choices
are extensively reliant on choices to participate in activities. Travel is a demand
derived from individual’s needs to perform out-of-home activities. Focusing on
activities allows one to take into account the interactions between persons in the
households and constraints imposed on activity schedules that emerge, for example,
from limited opening hours of facilities, working times and household needs (e.g.,
escorting a child to school). Therefore, there is generally a case of inter-dependency in
the travel choices and activity-agenda choices between persons within the same
households. Individuals as households’ members, particularly heads of household,
should play an important role in modeling activity and travel decision making. Their
decisions to engage in out-of-home activities, for example, often depend on household
needs and should take into account the presence of children, if any, accessibility, car
availability, etc. An out-of-home maintenance activity may be conducted
independently or jointly and household tasks often need to be allocated to household
members.
Although the need to incorporate household decision making has been acknowledged
from the beginning, this topic has only recently received little attention, and a
44
comprehensive model system at this level is still missing (Zhang et al., 2005, for
examples and Timmermans, 2006, for a review paper). Several works have been made
on the interactions of individuals within households (Gliebe and Koppelman, 2002,
2005; Scot and Kanaroglou, 2002; Srinivasan and Bhat, 2004), nevertheless, fewer
attempts to integrate the interactions in activity-scheduling models. Activity-scheduling
models share an objective to predict the sequence of decisions that leads to an observed
activity pattern of a household/individual. Activity-based models aim at predicting on a
daily basis and for a household which activities are conducted, with whom, for how
long, at what time, the location, and which transport mode is used when traveling is
involved (Arentze and Timmermans, 2000, 2005, and Miller and Roorda 2003).
ALBATROSS is one of the few operational activity-based models incorporating
household decision making (Arentze and Timmermans, 2000, 2004, 2005). It is a rule-
based computational process model developed for The Dutch Ministry of
Transportation, Public Works and Water Management. ALBATROSS differs from
other models, which use utility maximization as a framework for modeling activity
scheduling decisions. In contrast, ALBATROSS uses IF-THEN rules as a formalism to
represent and predict activity-travel choices of individuals and households. The
decision rules are extracted from activity diary data in the form of a decision tree by
using a CHAID-based decision tree induction method.
The objective of this thesis is principally to improve the ALBATROSS model to
elaborate household decision making more explicitly so that the interaction between
persons in the same household can be captured well. To accomplish these goals, some
components are elaborated, in particular in term of resource allocation, task allocation
and joint activity participation and car allocation. The structure of this thesis follows
the process model underlying ALBATROSS.
The purpose of this chapter is to give an overview of the research framework. Firstly,
we will review the ALBATROSS model and household decision making. It is followed
by introducing the new version of ALBATROSS that is developed in this PhD thesis.
In this section, how household decisions were modeled in the original ALBATROSS
model is also discussed. This is the state of the art against which to position this PhD.
Subsequently, the decision tree induction method is explained and finally, it ends up
with the conclusion.
3.2 ALBATROSS AND HOUSEHOLD DECISION MAKING
An important dissimilarity between the utility-maximizing models and a rule-based
model, such as ALBATROSS (Arentze & Timmermans, 2000, 2004), is that the former
45
models predict the choice probability of multi-faceted choice alternatives defined by
the modeler. In contrast, rule-based models do not a priori assume certain multi-faceted
choice alternatives but induce choice rules, based on a process model, for specific
choice facets. Activity-travel patterns that emerge are not a priori assumed and
classified.
Keeping this in mind, ALBATROSS simultaneously generates activity schedules for
individual household members, in which activity selection of one household member
depends on the activity schedule of the other adult, if any, in the household. In case the
number of cars is less than the number of drivers, a decision tree, representing choice
heuristics is used to assign the car to particular activities/trips of household members.
The result serves as one of the condition variables for other choices and car use is
systematically traced throughout the prediction of activity-travel schedules to create
dynamic choice sets/action spaces, which are used to check for any violations of space-
time constraints.
Joint activity participation are not separately and explicitly modeled. However, bring
and get activities (and sub-classifications if so desired) constitute one class of activities,
while travel party is a structural choice facet of the model, implying that ridesharing,
escorting and chauffeuring are endogenously generated by the model. In addition, it
means that these household activities are also predicted in terms of all other choice
facets included in the same (timing, duration, location, trip chaining, and transport
mode). Having said that, several aspects can be further improved.
Another partly rule-based model is Tasha (Miller & Roorda, 2003). Although not fully
operational yet, a prototype has some unique features to warrant discussion. The model
uses a set of rules to generate schedules. Unlike ALBATROSS, these rules are not
derived from observations, but primarily based on expert decisions and involve
concepts such as priority and flexibility. In addition, an ad hoc fine-tuning algorithm is
used. Activity-travel patterns of household members are generated simultaneously to
allow for possible interaction between members. These joint activities require that the
activity has the same start time, duration and location for each household member
participating in that activity. Thus, a window of opportunity must exist or be created in
the schedules of all of household members taking part in that activity for it to be a
feasible joint activity. The authors acknowledge that several other types of intra-
household interaction exist, but leave that for future research.
This brief characterization of fully operational, comprehensive activity-based models
suggests that at best most of these models have only started to look at household
decision making and household-level activity-travel patterns. In earlier versions and to
46
some extent also in the latest versions, household characteristics have been
incorporated only as explanatory variables in individual-level models. Of course, this is
quite remote from a model of household decision making. Some improvement in
statistical analysis can and should be made by realizing the multi-level nature of this
problem (e.g., Goulias, 2002; Miller, Nurul & Kandker, 2006) but fundamentally this
only implies a marginal adjustment.
Incorporating mechanisms of household decision making should substantially improve
the consistency and interdependencies in activity-travel decisions as an alternative to
the more or less arbitrary breakdown of the multi-faceted decision problem, typical of
the four-step models. However, although the degree of complexity and the
sophistication of the econometric analysis have been substantially enhanced, at a more
fundamental theoretical level considerably less has happened. Separating out the
generation of activities and classifying certain patterns will at best allow us to capture
only some aspects of how households cope with the constraints of their physical and
social environments and organize their activities in time and space in an inherently
dynamic context. In other words, a better understanding of this process and the
underlying mechanisms and determinants is required. Fortunately, the amount of
analytical research and modeling of specific sub-problems has increased rapidly over
the last couple of years. This research will be summarized in the following sections.
3.3 THE NEW VERSION OF ALBATROSS
ALBATROSS has been developed in consecutive phases for couple of years. In every
phase, particular elements of the model system were improved and more than a few
empirical tests were conducted. There have been 4 versions developed so far, and the
newest one, Version 5, is the one that will be developed in this PhD thesis. Version 1
(Arentze and Timmermans, 2000) was based on a limited data set, involving
approximately 3,000 person-days, collected in the South Rotterdam region. This study
was primarily designed to assess the potential of an activity-based approach and the
ALBATROSS framework in particular. The essential nature of the approach however
never changed and a number of key components were developed during this foremost
project. In this version, similar to most activity-based models, fixed (work) activities
were taken as observed and used as anchor point.
The system is acceptable if the main objective is to evaluate the viability of the
modeling approach, however there is a drawback in prediction. Therefore, in Version 2,
the activity skeletons (mandatory activities, such as work, business and other
mandatory activities) were generated. Moreover, the decision rules of the model were
47
re-induced using a national level, pooled data set of approximately 10,000 person-days.
The shift from a regional to a national level also implied that a population synthesizer
was developed. Still in the first application, congestion pricing was developed. Because
such scenarios imply traveler response to external policy, an approach that linked
stated response data to changes in the activity skeleton was developed. It serves as a
general approach to address such problems and use the ALBATROSS system for
policies for which historical data do not exist. These extensions are described in
Arentze and Timmermans (2005).
The application to congestion pricing revealed that rule-based models lack the detail
especially for continuous variables and cannot produce price and travel-time elasticity
of travel-demands satisfactory. Therefore, in Version 3, the principle of what is called
Parametric Action Decision Trees was developed (Arentze and Timmermans, 2007). It
was intended so that ALBATROSS can compete with discrete choice models in this
respect and can represent time and price elasticity and provide utility-based welfare
measures.
The enhancement of the model system went hand-in-hand with several studies and
applications that allow us to better judge the (relative) performance of the model. It
turned out that the activity-scheduling models outperformed the competing model but
the ranking between them is unclear or dependent on the criterion considered. It also
turned out that fair comparisons of completely different models are quite difficult.
They also conducted a spatial transferability study, and found that the model is
sufficiently robust to transfer decision rules derived from one region in the Netherlands
to other regions.
For the time being, the new travel survey in the Netherlands became available (MON).
Although this is not an activity diary, but rather a conventional travel survey, the data
could be transformed into an activity diary format and thus be used as input for
ALBATROSS. The advantages of this dataset compared to a particular purpose activity
diary data collection are clear. The data are collected on a continuous basis, includes a
larger sample of households which covers all of The Netherlands and all seasons of the
year and is less costly (as it is used for multiple purposes). Version 5, which is the
current version, has been developed based on this dataset.
ALBATROSS is one of the few of its generation that incorporate household decision
making aspects, by simulating the decisions of one household member, and then based
on the outcome of this, modeling the decision process of another household member.
ALBATROSS predicts for each household of a studied population the schedule of
activities and trips of each household head for a particular day. However, there are
48
some shortcomings in the model system. Although the approach involves household
decision making, individual schedules are generated. Consequently, the interaction
between persons in multi-person households in particular, was not captured explicitly.
Therefore the goal of this thesis, which is the refinement of ALBATROSS to Version 5,
is to systematically modeling household decision making processes in activity-based
modeling with a special focus on resource and task allocation and joint activity
participation.
3.3.1 Activity-Travel Diary Data
The empirical derivation of the model is based on activity diary data that is derived
from the Dutch National Travel Survey (MON=Mobiliteit Onderzoek Netherlands).
The data used was collected in 2004 covering all of the Netherlands. The survey is
conducted on a regular basis to obtain travel and activity information of residents in the
Netherlands. Although it is a one-day travel diary, the collected data is more complete
regarding activities at trip destinations than its predecessor travel survey called OVG. It
is a household survey where data is collected of all household members on the diary
day as well as general information about household and individual attributes such as,
gender, age, vehicle ownership and driving license ownership, home location,
individual income, occupation, number of working hours per week, etc.
Additionally, respondents are invited to give information about all trips made on a
designated day as also reported. All in all, this survey provides an exclusive data
source to analyze activity-travel behavior of Dutch residents. In the data collection,
29,221 households filled out a one-day travel/activity diary and 28600 of these
households fit the criteria for being considered in ALBATROSS. The data were
transformed to an activity-diary data format for the current estimation purpose.
In ALBATROSS, the classification of activity type is shown in Table 3.1. Ordinarily,
the activity types are grouped into fixed activities and flexible activities. Mandatory
activities are considered fixed activities, while non-mandatory activities are termed
flexible activities. Given the purpose of modeling household decision making in an
activity scheduling process, we distinguish the category of flexible activities as
household task activities and non-household task activities. Mandatory activities
include work, business and other mandatory activities (e.g. school, etc). A household-
task activity refers to an activity that can be allocated to different household members.
A non-household-task activity is a discretionary activity that can be conducted anytime
by any person in the household.
49
TABLE 3.1 Classifications of Activities in a Household in ALBATROSS
No Activity Types Group of
Activity
Person (P)/
Household
(HH) Level
Activity Representation
1 Work P Full-time and part-time
2 Business Mandatory
P Work-related
3 Bring/get person P/HH Drop-off/pick-up children or spouse
4 Shop-1-store P/HH Shopping 1-store
5 Shop-n-store P/HH Shopping multiple stores
6 Service-related
Household-
Task
P/HH Renting movie, getting (fast) food, institutional
purposes (bank, post office, etc)
7 Social P/HH Meeting friends/relatives, religions, social
activities, etc
8 Leisure P/HH Sports, café/bar, eating out, recreational activities
with children, movie, museum, etc
9 Touring
Non-
Household-
Task
P/HH Making a tour by car, bike, or foot (e.g. letting out
the dog)
10 Other Mandatory P Other mandatory activities (school, etc)
TABLE 3.2 Socio-Economic and Situational Attributes used in ALBATROSS
Label Definition Levels
Urb Urban density 0: most densely, 4: least densely
Day Day of the week 0: Monday, 6: Sunday
Comp Household composition 0: single 0 workers, 1: single 1 worker, 2: double 1 worker,
3: double 2 workers, 4: double 0 workers
Child Age of youngest child 0: no children, 1: <6, 2: 6 – 11, 3: 12 – 17 yrs
Age Age category of person 0: <35, 1: 35 – 54, 2: 55 – 64, 3: 65 – 74, 4: 75+ yrs
SEC Socio-economic class (in €) 0: <16,250 (low), 1: 16,251 – 23,750 (low - mid),
2: 23,751 – 38,750 (mid – high), 3: 38,750+ (high)
Ncars # of cars in household 0: no cars, 1: 1 car, 2: 2 or more cars
Driver Person has driving license 0: is not a driver, 1: is a driver
Gend Gender of person 0: female, 1: male
Wstat Work status of person 0: no work, 1: <32 hours week, 2: 32+ hours week
Pwstat Work status of person’s partner 0: no work, 1: <32 hours week, 2: 32+ hours week
50
TABLE 3.3 Accessibility Measures used in ALBATROSS
Label Definition Levels
nEmp1 Daily goods sector: # employees 0: <=115, 1: <=253, 2: <=307, 3: <=507, 4: <=675, 5: >675
within 3.1 km
nEmp2 Non-daily goods sector: # employees 0: <=395, 1: <=635, 2: <=762, 3: <=938, 4: <=2525, 5: >2525
within 4.4 km
nEmp3 All sectors: number of employees 0: <=8785,1:<=12995, 2: <=16120, 3: <=20199, 4: <=70314,
within 4.4 km 5: >70314
SizePop Size of population within 3.1 km 0: <=5050, 1: <=8845, 2: <=13217, 3: <=16833, 4: <=22884, 5: >22884
Dist1 Daily goods sector: distance within 0: <=71, 1: <=127, 2: <=165, 3: <=202, 4: <=346, 5: >346
which 160 employees work
Dist2 Non-daily goods sector: distance 0: <=92, 1: <=145, 2: <=176, 3: <=258, 4: <=334, 5: >334
within which 260 employees work
Dist3 All sectors: distance within which 0: <=92, 1: <=128, 2: <=201, 3: <=274, 4: <=360, 5: >360
4500 employees work
Dist4 Distance within which 5200 people 0: <=0, 1: <=105, 2: <=126, 3: <=163, 4: <=278, 5: >278
live
Household-tasks include the activity types in order of priority: (1) bring/get person, (2)
shopping (one-store), (3) shopping (multiple-stores), and (4) service-related activities.
Non-household-tasks include the following activity types also in order of priority: (1)
social visits, (2) leisure activities (other than touring), and (3) touring (by car, bike or
on foot).
Table 3.2 shows the situational and socio-demographic variables that are used as
prediction variables in ALBATROSS. These variables are the major variables that are
mostly used in the prediction of every choice facet in person-level and household-level
decision making. Obviously, gender is not used as variable in a model that needs joint
decision making. The variables that relate to household-level attributes are urban
density, day of the week, household composition, the presence of young children in the
household, socio-economic class, and car ownership. The remaining variables are
person-level attributes. In addition to that, a set of household-level variables relates to
measures of accessibility of locations given the home location of the household. These
are shown in Table 3.3.
51
3.3.2 The ALBATROSS Process Model and Extension to Include Household
Decision Making
ALBATROSS is an operational system of travel demand modeling underlying activity-
based approach. The model fits into the activity-based approach which is aimed at
predicting which activities are conducted, where, when, for how long, with whom, and
the transport mode involved. Although it does consider the activity-travel interaction
between persons in multi-person households, however, the decision mechanisms are
defined at the individual level. Scheduling steps are made alternately between the
household heads whereby the condition of the schedule after each decision step of one
person is used as condition information in the next decision step of the other person,
and vice versa. As a result, it only has implicit explanation about travel party as well as
joint activity. Additionally, car allocation between household members, in a case of
car-deficient household, is imperfectly treated. Furthermore, activity allocation, in
terms of household task activities, and joint activity participation are not addressed as a
household decision. Taken as a whole, the existing ALBATROSS does not cover the
household decision making explicitly. We therefore intend to accomplish the
shortcomings by improving some aspects in household decision making more
explicitly.
The recent ALBATROSS consists of two major components that together define a
schedule for each individual and each day. The first component generates an activity
skeleton consisting of fixed activities and their exact start-time and duration. The
second component determines the part of the schedule relating to flexible activities that
are conducted that day, their travel party, duration, time-of-day and travel
characteristics. Both components use the same location model. All components assume
a sequential decision process in which pre-defined rules operate to define choice sets
and implement choices in the current schedule.
In order to better capture within-household interactions, we identify the facets of
activity-travel behavior of the two household heads that require household decision
making and expand the household activity-travel scheduling process regarding each
component. The three major components are expanded to be five major components as
can be seen in Figure 3.1. In addition, in some parts of generation modules,
supplementary choice facets covering joint decision making are inserted. The five
major components and each component consisting information of the preceding
component, activity-level and schedule-level information as well as individual and
household attributes.
Figure 3.2 is a description of the generation module in Figure 3.1. It consists of three
components, i.e., generating the work activity, generating the work-related activity, and
52
generating the non-work activity. Each component is considered as person-level
decision making when the activity is conducted independently, and otherwise as
household-level decision making when the activity is conducted jointly. Initially, it
begins from the generation of work activities consisting of maximally two work
episodes of each person (male and female) in the same household. In this particular
component, the model is done exclusively from other models, considering a priority-
based activity scheduling process in that work activity lies in the top most priority in
the hierarchy. Both decisions that are done either at person-level or at household-level
are taken into account in this regard. At the person-level, it involves the choice of
number of work episodes, start-time and duration, and the location of each work
episode (tour). The decision of allocating the car to the work tour, in particular in car-
deficient households, on the other hand, lies at the household-level. Having allocated
the car, if necessary, the household heads face the decision of choosing the transport
mode to the work place which lies at the person-level.
The next stage of the first component defines the cohort of work-related (business and
other mandatory) activities starting from the generation activity concerned and its
number of episodes. Further, the duration and start-time of work-related activities are
determined. Given that work-related activities are sometimes linked to work activities,
the decision on whether or not there is link to work activities is defined afterward. The
last part in this stage is defining location choice. All facets in this step are determined
at the person-level.
The final stage in the first component deals with the facet of non-work activities.
Selecting the non-work activity is performed initially; yes or no a particular activity is
conducted by the two household heads, each for independent and joint participation
cases. In case of household task activities, it is followed by the allocation decision in
the subsequent step, whether a particular activity is done by the male, female, or both.
Further, the duration and start-time of each independent and joint activity is determined.
Having defined the first component, trip-chaining choices are made, as shown in
Figure 3.1. It defines the duration and start time of the activity concerned both at the
household-level and person-level. Further, the non-work tour is accomplished that
consists of car allocation and transport mode. The car allocation decision to non-work
tour is done at the household-level while the transport mode choice to non-work tour is
done at the person-level. It is essential to note that in all decision tree models, the
results of earlier decisions are used as condition variables for each next decision and
the process results is a complete schedule of each person.
53
Transport Mode to Non-Work Tours
Car Allocation to Non-Work Tours
Location choice
Trip-chaining choice
Generation modules
Generating Work Activity
• Person-Level : # episodes, Duration, Start time, Location
• Household-Level : Car allocation to work tour
• Person-Level : Transport mode to work tour
Generating Business & Other Mandatory Activity
• Person-Level : # episodes, Duration, Start time, Link-Work, Location
Generating Task Activities and Non-Task Activities
• Household-Level:
- Activity selection of joint activity categories
- Activity allocation (for task activities)
- Duration and Start time
• Person-Level:
- Activity selection of independent activities
- Duration and Start time
FIGURE 3.2 Generation Modules in ALBATROSS
Integration
FIGURE 3.1 Main Steps in the Scheduling Process of Current
ALBATROSS
54
# episodes J
No Yes
Duration ratio j = 1,2
Start time of episode j = 1
l = 1
Car allocation to Work tour l
l = l + 1
l < L
l = L
STOP
k = 1
Transport mode to Work tour k
k = k + 1
k < K
k = K
STOP
START
START
STOP
1
2
3
5
6
Include work
Work duration
J = 2
Duration of break
J = 1
STOP
4
Go to Location to work place module in Figure 4
14
15
Include business and other mandatory act i
# episode J
Duration of ep. j act. i
Position of j
Link ep. j to work
Start time ep. j act i
Yes
Yes
No
No
i = 1 No i = i + 1
j = j + 1
j < J j = J i = I
START
STOP
STOP
j = J, i < I
16
17
18
19a
19b
START
FIGURE 3.3 The Process Model for Mandatory Activities
Modified after: Arentze and Timmermans (2005)
20
55
START
Relative location of
episode i, j
i = i + 1
21
STOP
i = 1
Size by distance band
of episode i, j
22
Select location from band
OTHER
Same as PREVIOUS
Same as NEXT
j = 1 j = j + 1
i < I, j = J
i = I, j = J j < J
j < J
Same as previous location
In home municipality
Order of municipality
Distance band municipality
Order of zone in municipality
Distance band of zone in mun.
Nearest mun. of chosen order
i = 1
j = 1
j = j + 1
i = i + 1
j < J
i < I, j = J
i = I, j = J
i = I, j = J i < I, j = J
j < J
Yes
Yes
Yes
No
No
No
7
8
9
10
11 12
13
Go to Car allocation to work tour module in #14
START
STOP
STOP
Go to Car allocation to work tour module in #14
FIGURE 3.4 The Process Model for Predicting Locations of Work Activities
Source: Arentze and Timmermans (2005)
FIGURE 3.5 The Process Model for Predicting Locations of
Work-Related and Non-Work Activities
56
START
i = 1
j = 1
Trip-chain of
episode i, j
j = J, i = I
j < J
j = j + 1
i = i + 1
Car allocation to
Non-Work tour l
l < L
l = L
STOP
k = 1
Transport mode to
Non-Work tour k
k = ki + 1
k<K k=K
START
STOP
START
29
34
35
STOP
l = 1
l = l + 1
No Include non-work activities
Independent/Joint
Duration Joint
Start time Joint
If Joint, m = 1
Duration Independent
Start time Independent
START
No
If Independent, n = 1
Yes, i = 1
STOP Next activity
Yes, if task activity
Yes, if non-task activity
23
24
25
26
27
28
Go to Figure 5
Include a next episode of the current activity
Yes/No
j = 1 i = i + 1
j = j + 1 j = j + 1
i = i + 1
i < I, j < J, m = M
i < I, j = J, m = M
i = I
m = m + 1 If m = M, n = 1 n = n + 1
FIGURE 3.6 The Process Model for Non-Work Activities
57
After completing the whole process from generation module to transport mode to non-
work tour model, the process is finished. Nevertheless, in order to define the accuracy
of model, we compare the integration model that simulates the comprehensive model
between old version and new version. It is expected that the prediction result of new
version is higher than the old one.
Figures 3.1 – 3.6 schematically present the structure of each of the main components of
the model in a more detail. Each numbered rectangle corresponds to a decision tree to
be derived from activity diary data. The indices used in the figures are defined as:
i index of activity in order of priority, i = 1…..I j index of episode of activity I in order of start time, j = 1…J
k index of tour in order of start time, k = 1…K (person-level)
l index of tour in order of start time, l = 1….L (household-level) m index of joint activity in order of priority, m = 1….M
The mandatory component comprises decisions 1-9, where the work activity deals with
decision 1-6 and the work-related activity cope with decision 7-9. Non-work activity
that consists of household-task and non-household-task activity copes with decision
10-18. The activity scheduling process is done sequentially from the first component to
the subsequent component. All components in mandatory activity is done at the
individual-level decision making, only car allocation decision to work tour deals with
joint decision making. Given our purpose on household decision making, the
component of non-work activities is not only dealing with person decision making, but
also with household decision making. The location decision for work tour is done
separately with non-work tour.
In the diary data used for estimation, a joint (non-work) activity in a household is
identified as a particular non-work activity that occurs in the diary of both the male and
female head and takes place at the same duration (+/- 15 minutes). Joint activities have
priority over independent activities and hence are scheduled first.
3.3.2.1 The Mandatory Activity Module
As an important feature, the mandatory model component (Figure 3.3) determines
activity patterns that consist of several sub processes including:
1. Determining the pattern of work activity
2. Determining the location of work activity
3. Determining the car allocation decision to work tour
58
4. Determining the transport mode to work tour
5. Determining the pattern of work-related and other fixed activities
6. Determining the location of work-related activity
The work activity has maximally two episodes. The pattern is defined by decisions
about the number of episodes, duration and start-time. The duration and start time is
done as a continuous variable. The location component developed a location choice
model in which location choice decisions are made in a priority order of activities and
within activities in the order in which the activities occur in the schedule. To better
understand activity choice location in the context of a complete activity schedule for a
day, the concept of detour time is applied. This concept is used in ALBATROSS
(Arentze and Timmermans, 2007). Different from any other concept that consider the
travel distance from home or non-home to a particular location, detour time considers
relative location to the previous and next activity. The detour time of a candidate
location for an activity is defined as the extra travel time required that implement the
activity in the context of the current activity schedule. This concept is very useful to
build trip chains and to simulate the emergence of feasible activity-travel schedules
that take space-time constraints into account.
In case of work or work-related (business and other mandatory) activities, the non-
work activities are not yet scheduled and the location of the next (work or work-
related) activity, if it is other than home, is still unknown. In such cases the model
assumes that the next location is the home location. On the other hand, in case of a
non-work activity, the location of the next activity is unknown if it is a non-work
activity of a same or lower priority for the same reason. Again, in such cases the model
assumes that the next location is the home, work location or the location of a higher
priority activity (what comes first in the schedule). Although these assumptions are
simplifications of reality, it is to be expected that they will not seriously affect the
performance of the model. At least, the model is able to take into account the location
relative to home and to a previous/next location in every location decision of a
sequential priority-based scheduling process and consequently it should better cover
interdependencies in these choices. A space-time prism is calculated for each location
decision defining the set of locations that are within reach given the space-time
constraints imposed by the interaction between the environment and the schedule.
Having identified the origin and destination for the activity considered the model
determines the locations, based on postcode areas, which are within reach, i.e. within
the prism. Since transport mode is unknown yet, the model calculates a preliminary
prism based on the fastest transport mode available in the time slot under concern. For
instance, if the person is a driver and the household owns car(s), and the car is not used
59
for a work activity of another household member in the same time slot, then the fastest
travel mode is the car. In case there is no car in the household, the fastest travel mode
is public transport in most cases.
Having identified the fastest transport mode, the shortest travel time across the road
network is determined. Furthermore, the (minimum) duration of the activity, the time
window and opening hours of required facilities at destination, are taken into
consideration. Time window is defined by the earliest possible departure time from the
origin and the latest possible arrival time at the destination. All in all, the resulting set
of locations meet an exhaustive set of space-time and resource availability constraints.
Note that, the location choice for work activity is done differently from other activities.
This conceptualization is similar to the current version of ALBATROSS. However, the
existing model only applies to the context of person-level decision making
(independent activities) while in this study we expand it to cover as well household-
level decision making (joint activities).
3.3.2.2 The Non-Work Activity Module
Figure 4 represents the last part of the models that deals with:
1. Determining the pattern of non-work activity
2. Determining the trip-chaining
3. Determining the location of work-related and non-work activities
4. Determining the car allocation decision to non-work tour
5. Determining the transport mode to non-work tour
Joint decision making takes part in almost component in this module, unless in trip-
chaining and transport mode. The pattern of non-work activity is classified by
decisions about the activity selection, which activity to be done in the context of
household-task and non-household-task activities. In case of household-task activities,
the process continues to a decision of task allocation on who should perform the
household task, either the male, female, or both male-female. Further, participation in
activity is taken into account. If the activity is done alone it is phrased as independent activity and other wise as joint activity when the activity is done together. After
defining those components, the duration and start time is done as a continuous variable.
Further, trip-chaining choice is taken into account. This process is done exactly the
same as in a previous version. As done in mandatory activity module, the location
decision is taken into account in the subsequent component. Note that, in case of
independent activity, the work-related activities (i.e. business and other mandatory) are
included. The process of doing it is also similar with what is done in work activity.
60
However, the special conditions are taken into account for joint participation. For more
detail, please refer to chapter 6 of this thesis.
Car allocation decisions are considered as an element of a more encompassing activity
scheduling process. A car allocation model is applied for two-heads households with
one car, or so called car-deficient households. If one car is available in the household
and both household heads are drivers, then the decision which person is going to use
the car involves a household-level decision. For instance, in case of work tour, if the
two persons undertake a work activity during the same time slot, a decision needs to be
made who can use the car for the trip to work. A car-allocation decision is needed not
only if the two heads in a household both have a work activity. Also, if only one of
them performs a work activity, it is still necessary to identify whether the worker uses
the car or not. The model also includes the option that none of the household heads
uses the car, but some other means of transport. Hence, the decision options are male,
female, or none. The process of allocating the car to the two-heads household is
basically the same for work tour and non-work tour. Note that, work tour is defined
when at least one work episode includes in a trip-chaining. On the contrary, non-work
tour is identified when work episodes is not appeared on a trip-chaining of male or
female. The work-related activity is also accounted for non-work tour. The last part of
this module, which is also the last component in the modeling, is transport mode to
non-work tour. This is also done similar to what is done on work tour and based on the
underlying concept of car allocation decision. The transport mode has 4 choices: car driver, car passenger, public transport, and slow mode (bike and walk).
3.4 DERIVATION OF DECISIONS FROM DECISION TREE
Every decision step in the process model is managed by decision tree, as represented
by a numbered rectangle in Figure 3.3 to Figure 3.6. Each decision tree is derived from
corresponding observations in the activity diary data set using a CHAID based
induction method. This section considers the decision tree induction method used to
determine decisions in the prediction stage, as explained in Arentze and Timmermans
(2005). Discrete and continuous choices are separately discussed.
3.4.1 Discrete Choices
The different levels at which decisions are to be made include the schedule, tour and
activity level. Accordingly, the definition of a case differs between decision trees. As
for example, the abstract illustration is assumed that at the given moment in the
decision process, a decision is derived for N cases. A decision tree defines a
classification function.
61
Pr (k | Xj) = f(Xj) [3.1]
where k is an index of leaf nodes of the tree and Xj is a vector of attribute levels for
given case j. Since the type of decision trees used here is crisp (as opposed to fuzzy
trees) and deterministic (as opposed to co-evolutionary trees), the probability of
assigning case j to node k is one or zero. The action-assignment rule comes into
operation after [3.1] and determines:
Pr (i | k) = f(qk , δj) [3.2]
where i is an index of discrete choice alternatives considered in the given decision tree,
qk is the choice probability distribution across alternatives at the k-th node and δj is a
zero-one vector indicating the availability of each choice alternative in case j. Note that,
where qk is a characteristic of the decision tree, δj is to determined for each case in the
prediction stage. The probability of selecting alternative i in case j is:
Prj (i) = )|Pr()|(Pr kiXkk j∑ [3.3]
Further, the probabilistic action-assignment rule f(qk , δj) used in ALBATROSS is
specified. To simplify notations, the subset of cases assigned to leaf node k is
considered and the subscript k in the symbols is dropped. The rule can be written as:
=
∑i iij
iijij
q
qp
δδ ji,∀ [3.4]
where pij is the probability of selecting choice alternative i in case j (at leaf node k), δij
is a zero-one variable indicating the availability of i in case j, and qi is the choice
probability of alternative i dictated by the decision tree (at leaf node k) and estimated
on the training set. As implied by this equation, probability pij is zero if i is not
available and equals the second term on the RHS of the equation otherwise.
3.4.2 Continuous Choices
In the process model, continuous decision trees describe duration and start time choices.
Rather than a choice probability distribution across discrete choice alternatives, these
trees describe a specific distribution of the continuous duration or start time variable at
each leaf node (Arentze and Timmermans, 2005). Thus, the continuous action-
assignment equivalent of equation [3.2] defines the function:
62
Pr (y | k) = f(Rk , Bj) y = 0, 1, 2, ..., 1440 [3.5]
where Pr (y | k) is the probability of selecting value y at leaf node k, Rk is a vector of
parameters defining the distribution at leaf node k and Bj is a set of tuples (b1, b2)
defining unavailable or blocked ranges [b1, b2)] on dimension y in case j due to
temporal constraints. Since times are measured in minutes and the schedule has a fixed
time window (of 24 hours), y has a pre-defined minimum and maximum. Furthermore,
we assume natural numbers for y.
Continuous decision tree used in ALBATROSS define distributions at each leaf node
in terms of m – 1 cut-off points and the minimum and maximum of the range. The cut-
off points divide the range into m intervals in such a way that an equal number of
training cases at the leaf nodes is observed in each interval. As a consequence of this
method, Rk specifies m+1 parameters. The number of elements of set Bj in a specific
case is zero if the complete range is available and bigger than zero if parts of the range
are blocked by constraints.
To define the probabilistic continuous action-assignment rule used in ALBATROSS,
we the following symbols are used. Let Pj (y) denote the probability of selecting y = 1,
…, 1440 in case j, m denote the number of equal-frequency intervals used in
continuous decision trees, di represent the width of equal-frequency intervals used in
continuous decision trees, di represent the width of equal frequency interval i, bij be the
width of the blocked part of equal frequency interval i in case j defined by the
combination of Rk and Bj and Pj (y) =1, if y falls in the unblocked part of the interval i and 0 otherwise.
∑=ij iyiyP )|Pr()Pr()( j∀ [3.6]
where Pr (i) is the probability of selecting EFI (equal frequency interval) i and Pr (y | i) is the probability of selecting y given i. Pr (i) is defined as:
j
i
ijiC
d
bd
mi
−=
1)Pr( i∀ [3.7]
The first term represents the a-priori probability of selecting i. Because EFIs represent
an equal number of cases, an equal number of cases, an equal probability is assumed
for all m EFIs. The second and third terms define a correction this equal probability.
The first correction is equal to the proportion of the available range in the EFI i and the
third factor makes sure that probabilities sum up to one across EFIs.
63
Similar as in the discrete case, we should include availability variables as potential
predictor variables in inducing the tree to reduce the bias to the extent possible.
3.4.3 Goodness-of-Fit Measures
In order to measure the performance of the decision tree, different goodness-of-fit
measures are used for discrete and continuous choice (Arentze and Timmermans,
2005).
3.4.3.1 Discrete Choices
There are two alternative goodness-of-fit measures for discrete choice decision trees.
First, the so called likelihood or probabilistic theta is conceptualized as:
)|(Pr)|Pr()Pr( ' kikikjek i∑ ∑>−= [3.8]
where e is the probability of correctly predicting the choice for any given case j in the
same sample, Pr(j -> k) is the probability that j belongs to leaf node k, Pr(i | k) is the
probability that choice i is observed in cases belonging to leaf node k and Pr’(i | k) is
the probability of predicting I in those cases. The probabilities on the RHS of [3.8] can
be found as:
n
fkj k=>− )Pr( [3.9]
k
ik
f
fkiki == )|(Pr')|Pr( [3.10]
where n is the total number of cases, fk is the number of cases at leaf node k and fik is
the number of cases at leaf node k with observed choice i. The n and f variables all
refer to the sample from which the tree was derived (i.e. the training set) so that the
probabilities Pr(j -> k) and Pr(i | k) are to be interpreted as estimates of true
probabilities for any sample of unseen cases.
The predicted and observed probabilities in [3.8] are the same due to the probabilistic
action-assignment rule used. Substituting [3.9] and [3.10] in [3.8] gives:
2
∑∑
=
ik
ik
k
k
f
f
n
fe [3.11]
and rewriting results in:
64
∑∑
=k
k
i ik
f
f
ne
2)(1 [3.12]
It should be noted that this measure assumes bias-free predictions. In reality, the
action-assignment rule takes the availability of choice alternatives into account and
therefore is more complicated than [3.10]. The actual rule is given by [3.4].
By comparing e with a null model, we can derive a measure of relative performance.
We consider as the null-model a decision tree consisting of the root node only. Then,
the likelihood or probabilistic theta for the null model can be found as:
∑=i
iie )(Pr')Pr(0 [3.13]
Or ∑∑ =
=
i ii
i fnn
fe 2
2
2
)(1
0 [3.14]
where fi is the overall frequency of choice i in the sample.
The quotient:
0
0
1 e
eeeincr
−
−= [3.15]
then indicates that the increase in likelihood as a ratio of the maximum increase that is
possible given the null model. Note that this indicator is comparable to the (log)
likelihood ratio commonly used as a measure of goodness-of-fit for conventional
discrete choice models.
The second measure is directly derived from the Chi-square statistic used as split
criterion. The tree as a whole defines as I x K frequency table, where I is the number of
choice alternatives and K is the number of leaf nodes. The Chi-square of this table can
be taken as a measure of dependence between tree condition (leaf nodes) and choice.
Because the value of Chi-square is dependent on sample size n, we use a
standardization to obtain a measure on a (approximate) 0-1 scale, known as the
contingency coefficient and defined as:
n
c+
=2
2
χ
χ [3.16]
65
where 2χ is Chi-square of the I x K frequency table and n is sample size. A zero value
indicates a zero association between condition and choice and a value of one a
maximum dependence. So, c can be interpreted as the discrete equivalent of the linear
correlation coefficient between predictions and observations.
3.4.3.2 Continuous Choices
For continuous choice decision trees only one goodness-of-fit measure is available.
This measure is directly derived from the F-statistic used as a split criterion. For the
tree as a whole, the F-statistic is calculated as:
Kn
sn
K
MmnF k kkk kk
−−
−=
∑∑ 22 )(/
1
)( [3.17]
where n is sample size, nk is number of cases at leaf node k, K is the number of leaf
nodes, mk and sk are the mean and standard deviation of the distribution at leaf node k
and M is the overall sample mean. Thus, F represents the ratio between between-group
and within-group variance. The higher the value, the stronger the dependence between
condition and choice is.
3.5 CONCLUSIONS AND DISCUSSION
This chapter described several studies applications in household decision making. The
comprehensive operational system of activity-based travel demand modeling is still
few exist until recently. Therefore, the development of ALBATROSS could enhance
the studies of activity-scheduling models in activity-based transport demand modeling.
The framework of ALBATROSS in the context of person-level and household-level
decision making is also explained. Using the new travel survey in the Netherlands
(MON) data, the data is transformed into an activity diary format and used as input for
ALBATROSS. There are some advantages of using this dataset in terms of a particular
purpose of activity diary data collection. The collected data were done on a continuous
basis, includes a larger sample of households that covers all regions in The Netherlands.
Incorporating household decision-making into activity-based models of transport
demand receives increasing attention in recent times. Problems such as activity
allocation, joint activity participation and resource allocation have in the past typically
been addressed separately, i.e. not in the context of a scheduling process or at most ad
hoc as part of a more comprehensive activity-based model of transport demand.
66
Decisions to travel jointly in multi-person households require joint decision between
persons.
ALBATROSS concerns about joint decision making matter. The out-of-home activity
needs synchronization between persons in particular in multi-person households. By
incorporating car allocation decision either to work tour or non-work tour, the model
gives such an improvement in a recent mode choice model. Those households that are
dealing with car allocation decision process (car-deficient households) have significant
influence to the choice of transport mode, in case of one of male/female get the car to a
particular location.
REFERENCES
Arentze, T.A. and Timmermans, H.J.P. (2000), ALBATROSS: A Learning-based Transportation Oriented Simulation System. EIRASS, Eindhoven University of
Technology, The Netherlands.
Arentze, T.A. and Timmermans, H.J.P. (2003), “Measuring Impacts of Condition
Variables in Rule-Based Models of Space-Time Choice Behavior: Method and
Empirical Illustration”. Geographical Analysis, 35, 24-45.
Arentze, T.A. and Timmermans, H.J.P. (2004), “A Learning-based Transportation
Oriented Simulation System”. Transportation Research Part B, 38, pp.613-633.
Arentze, T.A. and Timmermans, H.J.P. (2005), ALBATROSS 2.0: A Learning-based Transportation Oriented Simulation System. EIRASS, Eindhoven University of
Technology, The Netherlands.
Gliebe, J.P. and Koppelman, F.S. (2002). ”A Model of Joint Activity Participation
between Household Members”. Transportation, 29, pp.49-72.
Gliebe, J.P. and Koppelman, F.S. (2005), “Modeling Household Activity-Travel
Interactions as Parallel Constrained Choices”. Transportation, 32, pp.449-471.
Goulias, K.G. (2000, “Companionship and Altruism in Daily Activity Time Allocation
and Travel by Men and Women in the Same Households”. In Proceeding of TRB 200, Washington, D.C., US.
Miller, E.J., and Roorda, M.J. (2003), “A Prototype Model of Household
Activity/Travel Scheduling”. Proceedings of the 2003 Transportation Research Board, Washington DC, USA.
Scott, D., and Kanaroglou, P. (2002), “An Activity-Episode Generation Model that
Captures Interaction between Household Heads: Development and Empirical
Analysis”. Transportation Research B, 36B: 875-896.
67
Srinivasan, S. and Bhat, C. (2004), “Modeling the Generation and Allocation of
Shopping Activities in a Household”. Paper presented at the 83rd
Annual Meeting of
the Transportation Research Board, Washington, DC.
Zhang, J., Timmermans, H.J.P., and Borgers, A. (2005), “A Model of Household Task
Allocation and Time Use”. Transportation Research Part B, 39, 81-95.
68
Chapter 4
CAR ALLOCATION BETWEEN HOUSEHOLD
HEADS IN CAR-DEFICIENT HOUSEHOLDS: A
DECISION MODEL
Anggraini, R., Arentze, T.A., and Timmermans, H.J.P., 2008. European Journal of Transportation Infrastructure and Research, 8(4), pp.301-319.
ABSTRACT This paper considers car allocation choice behavior in car-deficient households explicitly in the context of an activity-scheduling process, focusing on work activities. A decision tree induction method is applied to derive a decision tree for the car allocation decision in automobile deficient households using a large travel-and-activity diary data set recently collected in the Netherlands. The results show a satisfactory improvement in goodness of fit of the decision tree model compared to a null model. Overall, the probability of males getting the car for work is considerably higher than that of female in many condition settings. However, activity schedule, spatial and socio-economic variables appear to have an influence as well. An analysis of impacts of condition variables on car allocation decisions reveals that socio-economic variables have only a limited impact, whereas attributes of the transportation and land-use system have a relatively big impact. The propensity of men driving a car to the work place is higher than that of women. However, the relative accessibility of the work location by bike compared to car appears to have a relatively large influence on who gets the car for work. Household income and presence of children also appear to have significant effects.
69
4.1 INTRODUCTION
One of the major indirect factors contributing to increasing traffic congestion in urban
areas and highways is the increase of household automobile ownership. The vast
majority of households own at least one car and an increasing number of households
own more than one car. It is of no surprise therefore that car ownership and vehicle
fleet choice is one of the areas in transportation research that has received much
attention. A complementary active area of research focuses on transport mode choice
analysis and modeling to shed light on preferences of individuals in choosing one
option among several modes available for the trips they make (Xie, et al., 2003; Miller,
et al., 2005). Despite the substantial amount of research on car ownership in general, the specific
question of who is getting the car for which activities in car-deficient households has
received much less attention. In this context, car-deficient households are households
where the number of drivers exceeds the number of cars. Consequently, we know
relatively little about the factors that play a role in this decision and about the decision
process by which household members arrive at a choice on who should use the car
(Hunt and Petersen, 2004; Vovsha and Petersen, 2007). A model of binary car-
allocation choice (to use car or not) made by the household members for each tour in
an integrated framework of intra-household car-use preferences has been proposed and
estimated by Petersen and Vovsha (2005). They clearly showed that car-allocation
decisions are inter-related with mode choice, joint travel arrangements, and schedule
adjustments.
Yet, the outcome of this decision does not only have a direct impact on transport mode
choice, but also has potentially important ramifications for activity-travel schedules of
individual household members. Action spaces allowed by different transport modes
vary substantially and therefore the generation, location and timing of activities and the
organization of trips into tours depends strongly on the transport mode. Critical
questions in better understanding this decision process include: how do households
make trade-offs between mobility needs of drivers and are there differences between
households related to socio-economic and situational variables? Current travel demand
models have paid little attention to address these car allocation decisions.
The decision which person will use the car is a complex decision in car-deficient
households in the sense that many factors may influence this decision. For example,
gender roles may imply that males are more likely to use the car than women are.
However, it may also be that in case the male is going to work for a long period of time
in a day, while the female has many errands to complete, the flexibility of scheduling
and rescheduling activities made possible by the car, may lead the household to decide
70
that the female will use the car. As argued by Bianco and Lawson (1996), women are
more dependent on the car than men because of their traditional responsibilities related
to childcare and household maintenance as well as their concern for safety. On the
other hand, due to a good provision of public transport and more dense cities, in
Western European countries, we often see that women who do not participate in the
labor force tend to use public transport or use slow modes. Apart from socio-
demographic variables, the relative accessibility of locations for activities by car will
have an influence.
Surprisingly, car allocation decisions have also not received much interest in the
activity-based (micro-simulation) modeling literature. To date, fully operational
activity-based micro-simulation systems include ALBATROSS (Arentze and
Timmermans, 2000, 2004, 2005), TASHA (Miller and Roorda, 2003), Florida’s
Activity Mobility Simulator (FAMOS) (Pendyala, 2004), based on the Activity-
Mobility Simulator (AMOS) (Kitamura et al., 1996), and the Prism Constrained
Activity-Travel Simulator (PCATS) (Kitamura and Fujii, 1998), and CEMDAP (Bhat
et al., 2004), and some projects that have been implemented in the US (Bowman, J.L,
2008; Vovsha, P., 2008). One of the reasons for developing activity-based models was
that typical response patterns to transport demand management involved household
decisions. Such responses could not be captured by trip-based models, at least not
explicitly, as they were founded on individual as opposed to household behavior. In
general, only few of the existing operational activity-based models are based on
household decisions, and this statement also applies to the car allocation decision.
In this study, we examine this emerging issue. The study focuses on households which
have fewer cars than drivers. Car allocation decisions are considered as an element of a
more encompassing activity scheduling process. A large number of factors that
potentially influence car allocation decisions in car-deficient households are considered.
These factors relate to variables of the activity schedule and space-time setting as well
as individual and household characteristics. In this study, we use ALBATROSS as a
framework to investigate the car allocation decisions as part of an activity scheduling
process. ALBATROSS is an operational activity-based model developed for the Dutch
Ministry of Transportation, Public Works and Water Management for travel demand
analysis. More specifically, the paper will report the conceptualization of the problem
and present empirical results of a car allocation model for two household heads. It is
assumed that before the car is allocated, participation in activities both at the household
and person level is known. If one car is available in the household and both household
heads are drivers, then the decision which person is going to use the car involves a
household-level decision. For instance, if the two persons undertake a work activity
during the same time slot, a decision needs to be made who can use the car for the trip
71
to work. Note that the outcome could also be that both will use another transport mode
for the work commute.
The paper is structured as follows. First, the next section briefly explains the
ALBATROSS scheduling process model that provides the framework for the car
allocation model. The sections that follow describe the data used for the analysis and
the proposed car allocation model. After this section, the results of empirical analyses
will be considered focusing on some descriptive statistics and the empirical derivation
of the model. The paper is concluded by drawing conclusions and discussing some
possibilities for future research.
4.2 ALBATROSS PROCESS MODEL
ALBATROSS stands for A Learning Based Transportation Oriented Simulation
System. The model considers household and personal activities and travel performed
on a particular day and generates a schedule for each household head. The model takes
into account the presence of children as an independent variable, but their activities are
not explicitly represented. Work activities are presumably primary fixed activities,
whereas several household activities and work-related activities, such as bring/get
person, business, and others are assumed as secondary fixed activities. Shopping,
social and leisure activities are called flexible activities. It should be noted that fixed
activities are also predicted. ALBATROSS consists of four major components that together define a schedule for
each household head for a certain day as displayed in Figure 4.1. It should be noted
that this describes the computational process model underlying the system merely in
main lines. The first component generates a work activity pattern consisting of one or
two work episodes, if any, and the start time, duration and location of each episode. It
also predicts the transport mode(s) used to travel to the work location(s). The second
component determines the part of the schedule related to secondary fixed activities
(bring/get person, business, and others). It determines which types of these activities
are conducted that day and how many episodes and for each episode the start time,
duration and location. Furthermore, it also determines whether particular trip-linkages
are made with the work activity, if any. The following component considers the scheduling of flexible activities. Almost
similar to the previous component, it predicts activity types, number of episodes of
each activity type and the start time, duration and location of each episode. The
sequence of activities and possible trip-chaining links between activities are also
determined in this stage.
72
Generating Work Activity
- number of episodes
- start time
- duration of each episode
- location of each episode
- transport mode to the work activity
START
Generating Secondary Fixed Activities
- which type of activity
(bring/get,business,other)
- how many episodes of each activity
- start time
- duration of each episode
- linkage to work activity
- location of each episode
Generating Flexible Activities
- which type of activity
(shopping,service,leisure,social,touring)
- how many episodes of each activity
- start time
- duration of each episode
- trip-chaining for all activities
- location of each episode
Transport Mode for Each Non-Work
Tour
STOP
FIGURE 4.1 Schematic Representations of Main Steps of the
ALBATROSS Process Model
73
The latter decisions relate to all activities in the schedule, not just the flexible activities.
Finally, the last component predicts the transport mode used for each tour (except for
tours that include a work activity; for the latter tours the transport mode is known as
the outcome of a higher-level decision). The car allocation model developed in this study predicts who of the two household
heads in car deficient households uses the car for a particular activity. As a case, we
focus here on the work activity given that this activity usually is mandatory, conducted
by one spouse individually (as opposed to jointly), tends to occupy a large part of the
day and may serve as a second base location for other activities besides the home
location.
We emphasize, however, that car-allocation decisions are not confined to the work
activity. In the last step of the ALBATROSS scheduling process (Figure 4.1), the trips
required for non-work activities and the way they are organized into tours are known.
In that stage, a mode choice is made for each non-work tour (chain of trips including
one or more activities). These choices are preceded by a car allocation decision as well.
Although we focus here on the work activity, the same methodology developed here is
used to model car-allocation decisions involved for non-work tours. A car-allocation
decision restricts a subsequent mode choice: if the car is allocated to an activity or tour
no further decision is needed and if the car is not allocated, then a choice is confined to
other modes then the car. Note that car sharing is still open as an option if the car has
not been allocated to an activity or a tour. In ALBATROSS, car sharing is represented
as a car-passenger option. In other words, the car allocation decision has implications
for the possibility of choosing the car-driver mode only, but leaves open the car-
passenger mode.
Because ALBATROSS uses a sequential decision process, to generate a schedule for
each household head, the information available for the car allocation model is limited.
At the moment in the process when the car allocation model generates decisions, the
schedules of the household heads regarding the work activity are known; the schedules
regarding other activities then are still unknown. This does not mean, however, that the
decisions cannot take requirements of other activities (which are scheduled in a later
stage) into account. An outcome of the decision may well be that the car is not used for
a work activity considering the household’s needs for other activities. For example,
presence of children is a condition variable the system can use to anticipate a possible
escorting activity for which a car is needed and, hence, may inform the system not to
allocate the car to a work activity (of one of the two partners). Due to the complexity of
the scheduling problem it is inevitable that the decisions are made in a particular
sequence.
74
4.3 DATA
The data used in this study originates from the so-called MON survey (Mobiliteit Onderzoek Nederlands – Mobility Research Netherlands) held in 2004. The MON
survey is conducted on a regular basis to obtain travel and activity information of
residents in the Netherlands, and although it primarily uses a trip-diary it includes
detailed data on activities (at destinations) as well. More specifically, it is a one-day
travel diary of a sample of households that contains information about each household
member. In addition, individual and household socio-demographics such as age,
household composition, education level, income level, vehicle availability, residential
location, and information about all trips made within 24 hours as well as out-of-home
activities at destinations of trips are collected. For each trip, respondents are asked to
report information about several attributes including type and duration of the activity at
the destination, departure time and arrival time, trip purpose, transport mode, and
origin and destination location. Furthermore, trip-chains can be identified. All in all,
this information provides a suitable source to analyze activity-travel behavior of Dutch
residents because activity and travel information are both revealed. In this data
collection, 29,221 households filled out a one-day travel/activity diary and 28,600 of
these households fit the criteria for being considered here (forms of group housing,
such as for example student housing, are excluded). The data were transformed to an
activity-diary data format for the present estimation purpose.
4.4 CAR ALLOCATION MODEL SPECIFICATION
As said, the car allocation model focuses on car deficient households (i.e., more drivers
than cars present) and a joint decision between the two heads (mostly, a female and
male). The total sample extracted from the MON data includes 28600 households.
Given the purpose of this study, only the following households and days are relevant:
(1) there are two heads in the household; (2) there is one car in the household; (3) both
heads are drivers and (4) at least one of the heads has a work activity on the day
considered. As it appears, 3,523 households (and days) fit these criteria.
The car allocation decision model is schematically shown in Figure 4.2. A car-
allocation decision is needed not only if the two heads in a household both have a work
activity. Also, if only one of them performs a work activity, it is still necessary to
identify whether the worker uses the car or not. Furthermore, the model includes the
option that none of the household heads uses the car, but some other means of transport.
Hence, the decision options are the male, female, and none.
75
TABLE 4.1 Defining Car Allocation Decisions in Households
No. Number of male’s
work episodes
Number of female’s
work episodes
Cases Number
of Cases
Number of car
allocation decisions
1 0 1 520
2 1 0 A
1437 1
3 0 2 132
4 2 0 B
520 2
5 1 1 C 1047 1 or 2
6 1 2 144
7 2 1 D
228 1, 2, or 3
8 2 2 E 68 1, 2, 3, or 4
Total Sample 4096
START
STOP Work is in
HH schedule
Both heads have
work activity
Car Allocation
cases: C, D & E
Car Allocation
cases: A & B
Y
Y
N
N
Allocated to Male,
Female or None k ≤ K
N
k = 1
k = k + 1
k-th = index of # car allocation decisions K = # car allocation decisions
k = 1
k = k + 1
Allocated to Male,
Female or None
STOP
k ≤ K
N
STOP
Y
FIGURE 4.2 The Process of Car Allocation Model for Work Tours
76
In order to determine how many times such car allocation decisions should be made in
a household on the day considered, we need to identify the number of work episodes
performed by male and female heads. Table 1 shows the car-allocation cases that can
be distinguished in that respect. Case A represents the situation that only one of the
heads conducts one work episode, leading to only one car allocation decision in the
household. In this case, one head may use the car, but also there is an option that
he/she may not use the car. In Case B, two work episodes are included for only one of
the household heads (for example, he/she returns home for lunch). This situation thus
involves two car allocation decisions when the break is long enough to allow for
traveling back home and back to work again. In case C, both heads have one work
episode, implying that one or two car allocation decisions have to be made by the two
persons. One car allocation decision is to be made if the work episodes of the two
heads overlap in time (taking travel times into account). On the other hand, when there
is no overlap in time, 2 car allocation decisions have to be made.
The same principle of overlapping episodes also applies to Case D and Case E, leading
to maximally 3 and 4 car allocation decisions respectively. For example, in Case D,
when the male worker has 2 episodes and female worker has 1 episode, there are 1, 2,
or 3 car allocation decisions involved. In case both the first and second work episode of
the male are overlapped with the work episode of the female, then there is only 1 car
allocation decision required. If the first episode of the male worker and the episode of
the female worker are overlapped while the second episode of the male worker is not
overlapped with the episode of the female worker, this would imply 2 car allocation
decisions. Furthermore, if none of the two work episodes of the male are overlapped
with the female’s one, then 3 car allocation decisions are needed. The similar reasoning
applies to Case E. In the stage of the activity-scheduling processes where the work-
M
F
M
F
M
F
A B
C
D
M
F
E
M
F
FIGURE 4.3 Examples of Distinguished Cases
77
related car allocation decisions are made, other activities have not yet been scheduled.
Therefore, other activities that, in the end, are possibly attached to the work activity are
not considered in this model.
In determining whether or not there is an overlap in time, the travel time has to be
taken into account as well. The travel time by car mode (across the road network) is
relevant here. First, the timing and duration of work episodes of the household heads
are derived and then the type of overlap is determined. Note that, travel time by car is
used because that is relevant for car allocation decisions. Further details are provided in
Section 4.5.
4.5 EMPIRICAL ANALYSES
In this section we describe the results of deriving a decision tree model for car
allocation choice. Before discussing these results, we will first consider some
descriptive analyses carried out to get a better understanding of the characteristics of
the sample after selecting car deficient households. Next, we briefly discuss CHAID,
which is the decision tree induction method we use to derive decision rules from the
MON data. To facilitate interpretation of decision tree results, we use a post-processing
technique called impact tables. The impact table technique will be briefly discussed in
the section that follows. Finally, in the last section, we discuss the results of the
induction of the car allocation decision tree model and the corresponding impact table.
4.5.1 Descriptive Analyses
As discussed above, only a subset of households is relevant for the car allocation model,
because the problem concerns car allocation to work activities in car deficient
households. A total of 3,523 households were selected from the MON data, yielding
4,096 relevant cases of car allocation decisions. To describe the final sample, some
further descriptive analyses were conducted.
TABLE 4.2 Distributions of Households across Household Composition and SEC
(%)
SEC Household Composition
Low Mid-Low Mid-High High Total
Double, One Worker 3.2 13.3 12.3 11.6 40.3
Double, Two Worker 0.8 11.7 19.7 23.9 56.1
Double, No Worker 0.9 1.2 1.0 0.5 3.6
Total Sample (4096) 4.9 26.2 32.9 35.9 100
78
TABLE 4.3 Distributions of Household Heads across Household Composition and
Work Status of Household Heads by Gender (%)
Work Status, Male Work Status, Female Household
Composition Non-
worker
Part-
time
Full-
time
Total Non-
worker
Part-
time
Full-
time
Total
Double, One Worker 10.8 1.9 27.6 40.3 29.5 3.2 7.6 40.3
Double, Two Worker 0 8.4 47.7 56.1 0 31.2 24.9 56.1
Double, No Worker 3.6 0 0 3.6 3.6 0 0 3.6
Total Sample (4096) 14.4 10.3 75.3 100 33.1 34.4 32.5 100
TABLE 4.4 Work Duration Statistics by Work Status and Gender
Male Female
Working
Status
Average
duration of
work activity
(min)
Standard
Deviation
(min)
Freq. Average
duration of
work activity
(min)
Standard
Deviation
(min)
Freq.
Part-time 293.78 245.20 422 207.09 219.70 1412
Full-time 373.63 235.71 3085 257.88 245.47 1331
Total 364.02 238.26 3507 231.73 233.90 2743
TABLE 4.5 Work Duration Statistics by Day of the Week and Gender
Male Female
Day of the
Week
Average
duration of
work activity
(min)
Standard
Deviation
(min)
Freq. Average
duration of
work activity
(min)
Standard
Deviation
(min)
Freq.
Monday 373.84 237.49 708 239.32 235.01 576
Tuesday 381.85 228.92 663 249.10 238.62 513
Wednesday 384.33 233.22 623 230.87 229.94 485
Thursday 367.19 235.38 683 226.62 231.05 541
Friday 356.01 241.31 635 223.60 236.36 461
Saturday 237.96 242.12 135 211.68 228.38 114
Sunday 172.83 230.95 60 155.15 219.24 53
Total 364.02 238.26 3507 231.73 233.90 2743
79
Table 4.2 displays the frequency distribution of households across household
composition and socio-economic class combinations after selection. High-level income
households are in the majority (35.9%) and consist most frequently of double-two-
worker households. Double means two adults (male-female) household. This is
followed by mid-high income (32.9%), mid-low income (26.2%) and low income
households (4.9%).
The distribution of household heads across household composition and work status by
gender is presented in Table 4.3. Over 75% of males are full-time worker. Females are
approximately equally distributed across the work-status categories (33.1%, 34.4% and
32.5% for no, part-time and full-time worker respectively). This suggests that gender
still plays an important role in work commitments and task allocation.
Table 4.4 shows the distribution of duration across work activities for male and female
heads by work status. Note that persons may conduct more than one work activity a
day; the figures presented refer to durations on a per-activity basis (as opposed to a per-
episode basis). As we can see, males on average work approximately one and a half
times as long hours than females per work activity. Furthermore, in each work status
group, the average duration of males’ work activity is higher than that of female. The
frequency of work activities conducted by full-time male worker is leading among its
class, as a result of the fact that 75% of the males work full-time. This also suggests
that gender still plays a significant role in household task allocation.
Finally, Table 4.5 describes the household heads work activity duration split up by day
of the week. As can be seen, on average, working hours of males is similar from
Monday through Friday, about 6 hours. Meanwhile, working hours of females is on
average about 3-4 hours during working days. Again, this result shows that on average
males work longer hours than females per work activity.
4.5.2 Decision Tree Induction
We applied a CHAID-based tree induction method to identify the decision rules that
can describe car allocation choice behavior. CHAID (Kass, 1980) generates non-binary
trees, i.e., trees where more than two branches can be attached to a single root or node,
based on a relatively simple algorithm that is particularly well suited for the analysis of
large datasets and probabilistic action assignment. Other commonly used decision tree
induction systems are C4.5 (Quinlan, 1993) and CART (Breiman et al, 1984). All these
methods use a recursive process of splitting the sample based on condition variables
into partitions that are as homogeneous as possible regarding the action variable (i.e.,
the car allocation choice in this case). CHAID relies on the Chi-square test to
80
determine the best next split at each step. To determine the best split at any node, it
merges any allowable pair of categories of the condition variable if there is no
statistically significant difference within the pair with respect to the action variable.
This is done for each candidate condition variable. The split having the highest
significance value (after Bonferroni correction for multiple tests) across condition
variables is selected and implemented. The process is repeated until no more
significant splits are found also taking into account a pre-defined minimum number of
cases requirement at leave and parent nodes. This process of extracting rules is the
same as the one used in the ALBATROSS model. In order to develop the decision tree,
75% of the cases were used for training and the remaining cases were used for
validation. Generally, in deriving ALBATROSS decision models, attributes of the
household, person, space-time setting and schedule as far as known in the stage
considered of the assumed decision process are used as condition variables.
Observations of condition variables and action variables (car allocation choice) in each
case are extracted from the diary data.
The CHAID decision tree induction method allows one to define the threshold for
splitting in terms of a significance level for the Chi-square ( 2χ ) measure and a
minimum number of cases at leaf nodes. Alpha was set to 5% and the minimum
number of cases to 50. The number of leaf nodes gives an indication of the complexity
of the resulting tree. As a measure of prediction accuracy, the expected hit ratio is used.
The expected hit-ratio represents the expected proportion of cases predicted correctly
when a probabilistic action assignment rule is used. It is calculated as: ∑kik
ki
N
f
N
2)(1
where fki is the frequency of the ith action at the kth leaf node, N is the total number of
cases and Nk is the number of cases at the k-th leaf node. Note that the expected hit
ratio is comparable to a likelihood measure and, generally, yields lower scores than the
deterministic counterpart of the measure.
4.5.3 Deriving Impact Tables
Decision trees derived from data may become very large and complex and,
consequently, difficult to interpret. This holds true particularly for the present
application where the number of choice observations is very large. Arentze and
Timmermans (2003) developed a method to derive elasticity information from rule-
based models to facilitate interpretation, which we will use here to describe the results
of tree induction. The principle of the proposed method is straightforward. After
having derived a rule-based model from training data, the model is used to predict for
each condition variable a frequency cross table with the levels of the condition
81
variables in rows and the frequency distribution across the levels of the target variable
(i.e., the action variable) in columns. The frequency table for a given condition variable
is generated by applying the model as many times as there are levels of the condition
variable. In each run, each training case is assumed to take on the level considered on
the condition variable. The frequency distribution across actions of the action variable
predicted under that setting is recorded. Repeating this process for each level of the
condition variable yields a frequency cross table of the condition variable against the
action variable. The impact of the condition variable is then measured as the Chi-
square for this frequency table. Formally:
( )s sIS D= F [4.1]
where D is a Chi-square measure of the frequency table generated (Fs) for condition
variable s. This measure can be decomposed into a measure of impact on each level of
the action variable, as follows:
( )si siIS D= F [4.2]
where again D is a chi-square measure and Fsi is the vector of predicted frequencies of
the i-the action under the levels of the s-th condition variable.
Apart from impact size, we also use a measure of the direction of impact proposed by
Arentze and Timmermans (2003) defined as:
∑
∑
=
−
=−
−
−
=J
jjiij
J
jjiij
si
ff
ff
MS
2
1,
2
1,
||
)(
[4.3]
where fij is the predicted frequency of action i under the j-th level of condition variable
s and J is the number of levels. This measure can be interpreted as a measure of
monotonicity. If the condition variable has a monotonically increasing impact on the
frequency of action i across the levels of the condition variable, then MSsi equals 1 and
if it has a monotonically decreasing impact it equals -1. Any value in between these
extremes indicates that the impact is non-monotonous in the direction indicated by the
sign across the range of the condition variable. We emphasize that the monotonicity
measure is meaningful only for variables that are naturally enumerated; it is not
informative for variables that are purely nominal.
82
4.5.4 Condition and Action Variables
Table 4.6 portrays the condition variables that were used as input to the tree-induction
algorithm. The condition variables concern household level (including accessibilities),
individual level, and activity level variables (note that in this stage of the scheduling
process only work activities are known). Continuous condition variables, such as travel
time, duration, and parking price, are discretisized by using an equal-frequency interval
method which divides a continuous variable into n parts, in which each part contains
approximately the same number of cases.
The presence of young children in a household is taken as a condition variable as well
as other household and individual attributes, such as work status, socio-economic class
(in Euro), urban density (number of home addresses per area unit in the zone where the
household lives classified on a 5-point scale) and the day of the week (no. 1-8 in Table
4.6). The number of work activity episodes that is performed by male or female is 0, 1
or 2 episodes (no.9-11). Accessibility variables, such as travel time, train and bus
connections, parking price and free-paid parking place ratio were also used (no.12-29,
except no.18-19). They are calculated based on national datasets of the transport
system (car, bike/walk and public transport), parking facilities and land-use system
(employment data by sector and postcode area). They all relate to the trip to the work
location. If there is no work activity conducted by the person on a particular day, the
variables are set to zero for that person. If a work activity is conducted in the same
postcode area as where the person lives, then travel time is set to zero too. Travel time
by car is included as a direct measure of accessibility. Travel time ratios between
modes are used as indicators of relative accessibility by particular modes. Ratios are
used to allow the algorithm to identify impacts of modes more easily.
Work duration is an attribute of the activity for which a car allocation decision is made
(no.18-19). The definition of this variable takes the overlap pattern into account. To
explain this, consider for example, a case where the male has a work activity of 9 hours
and the female has two work episodes of 4 hours each with a one hour break in
between. In this case, there are two allocation decisions if the overlap concerns only
one of the female’s work episodes. For both decisions the considered work duration for
the male is 8 hours and for the female 4 hours. On the other hand, if the male’s work
activity overlaps with both female’s work episodes, just one allocation decision needs
to be made. For that decision the considered work duration for the male is 9 hours, as
before, but for the female it becomes 8 hours. Note that some of the variables relate to
the schedule level (a day of the household), whereas others are defined at the level of
the activity which involves a car-allocation decision (i.e., a work activity of one or both
of the heads).
83
TABLE 4.6 Condition Variables for Car Allocation Model
No Variable Classification Acronym
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
Urban Density
Household Composition
Presence of the youngest children
Day of the week
Age of person
Socio-economic class
Working status – M
Working status – F
Number of work episodes – M
Number of work episodes – F
Number of work episodes in household
Travel time by car – M (in minute)
Travel time by car – F (in minute)
Travel time ratio between PT and car –
M
Travel time ratio between PT and car –
F
Travel time ratio between car and bike
– M
Travel time ratio between car and bike
– F
Duration of work episode – M (in
minute)
Duration of work episode – F (in
minute)
Train accessibility – M
Train accessibility – F
Bus accessibility – M
Bus accessibility – F
Work conducted by male
Work conducted by female
Ratio # paid parking places to total #
parking places – M
Ratio # paid parking places to total #
parking places – F
Average price of parking – M
Average price of parking – F
Overlapping between two persons’
episodes
Number of car allocation cases in
household
Type of case for allocating the car
0=most densely , 4= least densely
2=DONEWORK, 3=DTWOWORK,
4=DNOWORK
0=no children, 1=<6, 2=6-11, 3=12-17 yrs
0=Monday to 6=Sunday
0=<35, 1=35-<55, 2= 55-<65, 3= 65-<75, 4=
75+ yrs
0=0-16,250 (low), 1=16,251-23,750 (low-mid),
2=23,751-38,750 (mid-high), 3=38,750+ (high)
0= non-worker, 1= part-time, 2= full-time
0= non-worker, 1= part-time, 2= full-time
0, 1, 2
0, 1, 2
1,2,3,4
0=0; 1=≤8; 2=9-14; 3=15-22; 4=>22
0=0; 1=≤6; 2=7-11; 3=12-18; 4=>18
0=0; 1=≤1.00; 2=1.01-1.98; 3=1.99-4.11;
4=>4.11
0=0; 1=≤1.00; 2=1.01-2.14; 3=2.15-4.49;
4=>4.49
0=0; 1=≤0.25; 2=0.26-0.37; 3=0.38-0.81;
4=>0.81
0=0; 1=≤0.30; 2=0.31-0.42; 3=0.43-1.00;
4=>1.00
0=0; 1=≤275; 2=276-520; 3=521-565; 4=>565
0=0; 1=≤240; 2=241-380; 3=381-540; 4=>540
0= no, 1= yes
0= no, 1= yes
0= no, 1= yes
0= no, 1= yes
0= no, 1= yes
0= no, 1= yes
0=0; 1=≤0.09; 2=0.10-0.15; 3=0.16-0.28;
4=>0.28
0=0; 1=≤0.07; 2=0.08-0.14; 3=0.15-0.24;
4=>0.24
0=0; 1=≤9; 2=10-25; 3=26-66; 4=>66
0=0; 1=≤8; 2=9-22; 3=23-44; 4=>44
0=no, 1=yes
1=1, 2=2, 3=3, 4=4
1=Male only, 2=Female only, 3=M&F (each 1
ep), 4=M (2 ep) & F (1 ep), 5=M (1 ep) & F (2
ep), 6=M&F (each 2 ep)
Urban
Comp
Child
Day
Age
SEC
WstatM
WstatF
NworkM
NworkF
NworkH
H
TTcM
TTcF
TTptM
TTptF
TTcbM
TTcbF
DurM
DurF
TrAcM
TrAcF
BusAcM
BusAcF
Mwork
Fwork
RParkM
RParkF
PParkM
PParkF
overlap
NcarAl
cases
Note: M = Male; F = Female; PT = Public Transport; ep = episode
84
The variables that correspond to the schedule level are number of work episodes of
male and female respectively and number of car-allocation-decision cases in a
household (no.31). The number of car allocation cases occurring in a household can be
1, 2, 3, or 4 cases (see Table 4.1 and Figure 4.3). The variables at activity level are the
following. For each car allocation case, the timing of work activities of both persons
have to be considered to determine whether or not there is an overlap in time (no.30).
Obviously, if only one person performs a work activity in a particular time period, then
there is no overlap in time, and otherwise there might be. Variable no.32 indicates the
type of overlap in terms of all possible combinations of number of work activities
(none, one or two) by male and female. In Cases (1) and (2) only the male or female
has a work activity in a particular time period. In contrast, in cases (3) to (6) there is a
time overlap between their work activities.
As a result, a total of 32 condition variables were defined. The action variable, as the
output of the car allocation model, involves assigning the car to male, female, or none
of the two household heads.
4.5.5 Results
For deriving the car allocation model for work activities, a total of 4,096 observations
could be derived from the data set. 75% of these cases (3,114) were used for training
and the remaining cases were used for validation. Of 4,096 cases, the probabilities of
the car being allocated to male and female are 37.28% and 17.77% respectively. In the
remaining cases, 44.95%, male and female heads use other modes to the work place.
Table 4.7 shows the frequency distribution of allocation decision outcomes over
household types in terms of work status of the heads. In households where male is a
non-worker, in about 50% of the cases household heads choose some other mode to
travel to the work place. In households where male is a part-time worker and the
female is a non-worker, the car is allocated to the male in about 43.59% of the cases.
However, if both male and female are part-time workers, about 50.75% of the cases
they use some other mode than car. In households where male is a full-time worker and
female is a non-worker, the car is allocated to the male in 43.67 % of the cases. In sum,
the figures show that the male gets the car more often than the female even in two-
worker households.
Given a minimum group size of n=50 cases at leaf nodes and a 5% alpha level, the tree
generated by CHAID consists of 29 leaf nodes (decision rules). The hit ratio (based on
85
a probabilistic assignment rule and the training set) of the model, compared to a null-
model (a root-only decision tree) indicates a significant improvement achieved by the
tree: the hit-ratio of the null-model of 0.374 is significantly increased to 0.540.
Figure 4.4 shows the resulting car allocation tree model graphically by branch from the
root note. The first split is implemented on travel time ratio between car and bike for
the work activity performed by female (TTcbF). Recall that the variable is set to zero if
the person has no work activity or the work activity takes place in the same post code
area as the home location. This results in five branches from the root. Branch #1
represents the condition where the female has no work activity or a zero travel time to
the work place. Within this node a split is implemented on travel time by car for the
male work activity (TTcM), and so on. The probability distribution across male, female
and none options is shown in italic font at each leaf node.
TABLE 4.7 Frequency Distribution of Work Status across the Action Variables
Work status % of getting the car No
Male Female Male Female None Total
1 Non-worker 32.88 15.75 51.37 146
2 Part-time 9.23 40.00 50.77 130
3
Non-worker
Full-time 36.10 13.74 50.16 313
4 Non-worker 43.59 16.67 39.74 78
5 Part-time 38.81 10.45 50.75 67
6
Part-time
Full-time 25.99 25.99 48.01 277
7 Non-worker 43.67 7.26 49.07 1129
8 Part-time 35.88 20.91 43.21 1215
9
Full-time
Full-time 36.98 27.13 35.90 741
Total 1508 747 1841 4096
Each path from the root to a leaf node represents a decision rule. For example, the path
printed in bold (Figure 4.4) represents the rule:
IF: TTcbF = 0 ∧ TTcM = 1 ∧ TTcbM = 0-2 ∧ DurM = 3-4
THEN: Male = 35.1%, Female = 0%, and None = 64.9%
The rule denotes that IF female either does not have a work activity or the work and
home location are in the same zone AND travel time (by car) of male is 8 minutes or
less AND the travel time ratio between car and bike for the male is less than 0.37
(traveling by car is at most 2.7 times as fast as the bike) AND male’s work duration is
at least 521 minutes (8.68 hours), THEN the probability that the male gets the car is
35.1%. Thus, the propensity of not using the car to work by male is as high as 64.9%
86
under these circumstances (where the male’s work location is relatively well accessible
by bike).
As another example, in branch #2, the rule printed in bold indicates that IF travel time
ratio between car and bike for female is less than 0.30 (relatively good accessibility by
car) AND the travel time ratio between car and bike for male is at least 0.26 (relatively
good accessibility by bike), THEN the probability of female getting the car (26.2%) is
yet lower than that of male (58.4%). This rule indicates that even if the male’s work
place is well accessible by bike, the propensity of the male to use the car is
considerably higher than that of female. Furthermore, in branch #3, as another example,
the rule printed in bold indicates that IF the female and male both have a work activity
and the travel time ratio between car and bike for the female is in between 0.31 and
0.42 (the car is between 3.2 and 2.4 times faster than bike) AND travel time ratio
between public transport and car of male is greater than zero AND there is a train
connection between home and the female’s work location, THEN the female’s
probability of getting the car is substantially higher than the male’s, namely 57.4% and
29.8% respectively.
The results of a performance analysis are shown in Table 4.8 in the form of a confusion
matrix for the training and validation set. A confusion matrix describes the model
performance in terms of a distribution of predicted choices for each observed choice
category in the data set. The confusion matrix shown is based on probabilistic model
predictions. The diagonal will have high numbers in case of good prediction. Off-
diagonal elements of the matrix indicate the probabilities of predicting wrong actions
for each observed choice category.
Table 4.8 shows that the model achieves a substantial improvement compared to a null-
model as diagonal cells have higher percentages. For example, as it appears in the
training set, in 37.7% of the cases we observe males using the car for the work activity.
In 54.3% of these cases the model predicts car allocation correctly, while for the
remaining cases the model predicts incorrectly that the female will use the car (9.2%)
and none of the heads use the car (36.5%). Note also that, due to the probabilistic
assignment rule used, the predicted distribution exactly matches the observed
distribution overall cases. In that sense the predictions are bias free. Comparing the
diagonals of the training and validation set suggests a small decrease in accuracy. As
the bottom-right cell shows, the overall accuracy on the validation set is slightly
decreased from 0.540 to 0.534. We consider the small decrease in accuracy as
acceptable.
87
To evaluate the quantitative impacts of each condition variable on the action variable,
Table 4.9 displays the impact table for the car allocation model. The condition
variables are listed in order of decreasing impact on the action variable overall (the IS
column). Note that ISmale, ISfemale, and ISnone show the size of the impact for each action
separately.
When we look at the differential impacts of types of condition variable, we see that
socio-economic variables have only a limited impact, whereas attributes of the
transportation system have a relatively big impact. Especially, travel time ratios and
TTcbF 4
Mwork 0
0; 0.500; 0.500
Mwork 1
0.474; 0.308; 0.218
Branch #3
Branch #4 Branch #5
Branch #2 TTcbF 1
TTcbM 0 TTcbM 1
0.613; 0.333; 0.053
SEC 0,1,2
0; 0.780; 0.220
TTcbM 2-4
0.262; 0.584; 0.154
TTcbF 2
TTptM 1-4 TTptM 0
TTcF 0-1
0; 0.220; 0.780 TTcF 2-4
0; 0.465; 0.535 TrAcF 0
0.442; 0.299; 0.259 TrAcF 1
0.296; 0.574; 0.130
TTcbF 3
TTcbM 0 TTcbM 1
0.712; 0.106; 0.182 TTcbM 2-4
TTcF 0-1
0; 0.203; 0.797
TTcF 0-1 TTcF 2-4
0.378; 0.378; 0.243
SEC 3
0; 0.574; 0.426
TTcF 2-4
0; 0.430; 0.570
TTcM 0-1
0.272; 0.141; 0.587
TTcM 2-4
0.547; 0.156; 0.297
TTcM 0 TTcM 1
TTcbM 3-4
0.315; 0; 0.685 TTcbM 0-2
TTcM 2 TTcM 3-4
Day 0,3
0.075; 0; 0.925 Day 1,4,2,5,6
0.267; 0; 0.733
DurM 0-2
0.539; 0; 0.461 DurM 3-4
0.351; 0; 0.649
TTcbM 2-4 TTcbM 0-1
0.735; 0; 0.265
SEC 2,3 SEC 0,1
0.614; 0; 0.386
PParkM 0-1 PParkM 2-4
Child 0
0.547; 0; 0.453 Child 1,3,2
0.320; 0; 0.680
TTcbM 0-3
0.728; 0; 0.272 TTcbM 4
0.536; 0; 0.464
TTcbF 0
TTptM 0-1
0.291; 0; 0.709 TTptM 2-4
0.535; 0; 0.465
Branch #1
FIGURE 4.4 Car Allocation Tree Model with 5 Major Branches
88
parking tariffs for the work location emerge with substantial impacts. The variable that
gives by far the biggest impact is the travel time ratio between car and bike for female
(TTcbF). The monotonicity measure (MSfemale = 0.33) clarifies that with increasing ratio
of this variable, the probability that the female gets the car increases. At first sight, this
seems implausible as the ratio indicates the relative accessibility by bike. However,
note that a value of zero of this ratio means that the female has no work activity or a
work activity in the home postcode area. Hence, an increase of the ratio from a zero
value means a change in condition from no travel to positive travel for the female and,
hence, an increase in the probability of getting the car.
The fact that the impact is non monotonous indicates that the probability does not
increase in the higher range, i.e. where an increase indicates an improvement of
relative accessibility by bike. The monotonicity measure for the variable that gives the
second biggest impact, TTcM, indicates that as travel time (by car) of male goes up, the
frequency of allocating the car to the male increases monotonically (MSmale = 1), as
expected. In sum, travel time and parking price variables have a big influence on car
allocation decisions between the two household heads in a car deficient household, as
indicated by the results of the first six variables.
In terms of socio-economic variables, we find that the most influential variable is
socio-economic class (SEC). Interestingly, the probability of getting the car decreases
monotonically for both male and female (MSmale dan MSfemale = -1.00) as income rises.
This result is somewhat counter-intuitive, given that car possession tends to be higher
among high income groups.
It should be noted, however, that since we consider car-deficient households we have
corrected for number of cars available in the household (we consider only double-adult
households having one car). Within this group, third variables such as education level
and availability of public transport at the work place may exert an influence. Income is
correlated with education level and possibly urban density at the location of
employment (larger cities) and the latter variables are correlated with use of public
transport. As a consequence, increasing income may lead to decreasing car allocation
to work activities. The probability of male getting the car increases when the male has
a work activity on the day concerned (Mwork). The presence of young children in the
household (Child) is the last socio-economic variable that has an impact on car
allocation decisions. Again interestingly, the tendency of not using the car by male
increases monotonically (MSnone = 1.00) when the value of this variable increases, i.e.
going from no children to presence of children with increasing age. Since there is at the
same time no influence on the probability that the car is allocated to the female, it
indicates that the car stays at home more often (possibly, for non-work activities of the
female).
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TABLE 4.8 Confusion Matrix for the Training and Validation Sets
Training set (N=3114) Validation set (N=982)
Predicted Predicted Observed
Male Female None Total Male’ Female’ None’ Total’
Male 0.543 0.092 0.365 0.377 0.524 0.099 0.378 0.360
Female 0.197 0.471 0.332 0.176 0.166 0.478 0.356 0.184
None 0.307 0.130 0.563 0.448 0.321 0.113 0.566 0.455
Total 0.377 0.176 0.448 0.540 0.365 0.175 0.459 0.534
TABLE 4.9 Impact Tables of Condition Variables of Car Allocation Model
No Variables IS ISmale ISfemale ISnone MSmale MSfemale MSnone
1 TTcbF 3719.77 120.71 2376.06 1223.00 -0.16 0.33 -0.38
2 TTcM 948.75 519.71 0.01 429.02 1.00 -1.00 -1.00
3 TTcbM 446.41 257.61 87.46 101.34 0.09 -0.20 -0.03
4 TTcF 58.58 0.14 43.07 15.37 -1.00 1.00 -1.00
5 PParkM 50.8 28.82 0.00 21.99 -1.00 - 1.00
6 TTptM 45.57 25.64 0.20 19.74 1.00 -1.00 -1.00
7 SEC 8.2 2.15 2.02 4.02 -1.00 -1.00 1.00
8 Day 5.69 3.10 0.00 2.59 0.33 - -0.33
9 DurM 5.11 2.79 0.00 2.32 -1.00 - 1.00
10 TrAcF 4.66 0.54 3.79 0.33 -1.00 1.00 -1.00
11 Mwork 3.41 2.02 0.92 0.47 1.00 -1.00 -1.00
12 Child 2.42 1.33 0.00 1.09 -1.00 - 1.00
As for the situational variables, day of the week (Day) is the most influential variable.
There is a non-monotonous tendency (MSmale = 0.33) of increasing probability of
allocating the car to the male as the week proceeds from Monday to Sunday (the lowest
value on this variable is Monday). Day of the week has no influence on the probability
of allocating the car to the female. Another variable that has no influence on the
probability of allocating the car to the female is work duration of the male (DurM). The
probability of the male getting the car decreases monotonically as his work duration
goes up (MSmale = -1.00). The presence of a train connection between home and the
female’s work location (TrAcF) increases the probability of the female getting the car
and decreases the probability that the male gets the car. Probably, the existence of a
train connection acts as a proxy for distance and urban density: a train connection
generally exists only between locations with relatively high density and far enough
apart.
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4.6 SUMMARY AND CONCLUSIONS
This paper considered car allocation choice behavior in car-deficient households
explicitly in the context of an activity-scheduling process. Focusing on work activities,
a car allocation model based on rules derived from a large travel diary data set using a
CHAID-based induction algorithm was presented. The face-validity of the decision tree
model is good in the sense that the derived rules and impacts of condition variables are
readily interpretable. The overall goodness-of-fit of the model is satisfactory. Although
the performance on a validation set decreased slightly, the set of decision rules seems
stable across training and validation set to a satisfactory extent.
The propensity of men driving a car to the work place is higher than that of women in
car deficient households, particularly, when women have no work activity or women’s
work place is in the same zone as the home location. This finding is consistent with a
common notion that women use a slow or public transport mode more often to travel to
activity locations. Similar to that, women tend to use the car when men have no work
activities or men’s travel time to work place is zero. When the female’s work location
is relatively well accessible by car, women are prevalent in getting the car.
In terms of decision rules results, in 43.1% of the rules men get the highest probability
to use the car while in only 20.7% of the rules women have the highest probability to
use the car. In the remaining of the rules (36.2%) none of the heads using the car gets
the highest probability.
As the impact table analysis showed, travel time variables and, in particular, the
relative accessibility of the work place by car compared to bike by far plays the most
important role in car-allocation decisions in two-driver, single-car households. Work
duration, day of the week and the existence of a train connection between home and
work location also has an impact on the decisions. Although socio-economic variables
appear to have only small effects on the decisions, presence of young children and
household income has an influence too.
As we showed, car allocation decisions can be modeled as an element of a more
encompassing activity scheduling process. ALBATROSS proved to be a suitable
framework for this. This focus of our approach meant at the same time that only a
limited set of explanatory variables at the level of the individual and household was
taken into account. From an analytical perspective, it is interesting to extend the set of
explanatory variables and investigate what the effects are of additional attributes such
as job characteristics and car characteristics on these decisions. Furthermore, given that
attributes of transportation systems appear to be significant, it is worth while to include
even more detailed descriptors of the transportation system, e.g. public transport
91
services and parking facilities. Finally, the present study focused on car allocation
decisions in relation to the work activity. Clearly, car allocation decisions may also
occur at the level of non-work activities in a scheduling process. The same approach
as developed in this study can be applied for that purpose.
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Chapter 5
MODELING JOINT ACTIVITY PARTICIPATION
AND HOUSEHOLD TASK ALLOCATION
Anggraini, R., Arentze, T.A., and Timmermans, H.J.P., 2008. Paper presented at the 10th International Conference of Advanced Application in Transport and Technology,
Athens, Greece
ABSTRACT This paper describes an empirical derivation of a household-level decision model of activity choice taking into account joint participation and task allocation between household heads. These are considered household-level decisions given that they involve commitments of multiple persons, in particular in two-head households. Attributes of households, for example the presence of young children and attributes of the work activities and space-time setting are considered as explanatory variables. To deal with this large set of attributes and account for non-linear relationships between the variables, a decision tree induction method is used to derive a decision tree model for each decision in an activity-scheduling process. Thus, we show how the decision tree models can be used as a component in an activity-scheduling model to predict travel demand in an activity-based micro-simulation system. The model shows a satisfactory performance in terms of goodness-of-fit on a hold-out set and face validity.
94
5.1 INTRODUCTION
Operational models of individual’s activity-scheduling behavior have begun to emerge
recently. Activity-scheduling models share the objective to predict the sequence of
decisions that leads to an observed activity pattern of a household/individual. Activity-
based models aim at predicting on a daily basis and for a household which activities are
conducted, by whom, for how long, at what time, the location, and the transport mode
that is used when traveling is involved (Arentze and Timmermans, 2000, 2005; Miller
and Roorda, 2003). There has been some research on the interactions of individuals
within households (Gliebe and Koppelman, 2002, 2005; Scot and Kanaroglou, 2002;
Srinivasan and Bhat, 2004), but fewer attempts to integrate the interactions in activity-
scheduling models.
The purpose of this study is to develop and test a model of households’ activity
participation decisions explicitly in the context of an activity-scheduling process.
ALBATROSS is one of the few operational activity-based models incorporating
household level decision making (Arentze and Timmermans, 2000, 2004, 2005). It is a
rule-based computational process model developed for the Dutch Ministry of
Transportation, Public Works and Water Management. In that respect, ALBATROSS
differs from other models, which use utility maximization or hybrid forms as a
framework for modeling activity scheduling decisions. In ALBATROSS, decision
rules for making scheduling decisions are extracted from activity diary data in the form
of a decision tree by using a CHAID-based decision tree induction method. The rules
predict actions in a probabilistic manner to reproduce non-systematic variance in
choice behavior.
The study focuses on two-head households and considers the joint decision making of
individuals related to household task allocation and joint participation in activities. We
propose an activity classification and identify the activity types that likely relate to the
needs at a household level and that can be allocated among the members. We use the
term (household) task activities to refer to these activities. In addition to decisions to
conduct and allocate task activities, the proposed model also predicts households’
decisions to conduct joint activities of a non-task nature on a given day.
The remainder of this paper is arranged into several sections. First, the next section
describes the proposed process model of activity-travel scheduling in the
ALBATROSS process model. Next, the CHAID algorithm that is used to induce
decision trees is briefly reviewed to give readers a better perspective of the
computational process model. Furthermore, the impact table that is used to measure the
size and direction of condition variables across action variables is described as well.
The subsequent sections describe the activity-travel data set used to derive the
95
decision-tree models and the results of deriving the decision trees from the data. The
paper concludes with discussing the major conclusions and remaining issues for future
research.
5.2 THE ACTIVITY SCHEDULING PROCESS MODEL
ALBATROSS (A Learning-Based Transportation Oriented Simulation Systems)
predicts for each household in a studied population the schedule of activities and trips
of each household head for a particular day. The activity scheduling process consists of
four major components: (1) work activity generation (including timing, duration,
location and transport mode choice for each work trip), (2) other fixed activity
generation (including timing, duration and location), (3) flexible activity generation
(including timing, duration and location), and (4) trip-chaining decisions and transport
mode choice for each tour. In the existing ALBATROSS model, interactions between
persons are represented only in a limited manner. Scheduling steps are made alternately
between the household heads whereby the condition of the schedule after each decision
step of one person is used as condition information in the next decision step of the
other person, and vice versa. Some aspects, such as activity allocation, car allocation,
and joint participation in activities and traveling, however, require joint decisions of
the two household heads (Anggraini, et al., 2007). In this study, we consider joint
decision making on the level of activity participation and show how this can be
modeled in the context of a scheduling process in a more elaborate way.
As the above-mentioned phases suggests, the activity types distinguished are grouped
into fixed activities and flexible activities. A fixed activity can be considered as an
activity that has to be done within a particular time horizon on a regular basis, due to
longer term commitments made by the individual. A flexible activity is an activity that
can be done freely at any time. Examples of fixed activities are work and escorting a
child to school, while most non-work activities are considered flexible activities. In
order to identify household-level decision making in activity scheduling and taking
into account available activity data, we cluster activities into 10 activity categories as
displayed in Table 5.1. These activities are similar to the classification used in the
current ALBATROSS model. Nevertheless, to distinguish person (P) and household
(HH) level activity-participation decisions, we subdivide each non-task activity
category into independent and joint activities. A task activity refers to a household task.
Bring/get person, shop-1-store, shop-n-store, and service-related activities (see Table
5.1) are considered task activities. A non-task activity just as a task activity can be
conducted anytime by any person in the household either independently or jointly.
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TABLE 5.1 Activity Classifications in a Household
No Activity Clustered
Activity
Personal (P) or
Household
(HH) Level
Scope of Activities
1 Work Work P Full-time and part-time
2 Business P Work-related
3 Other
Work-
related P Other mandatory activity (school, etc)
4 Bring/get person HH Drop-off/pick-up children/spouse to a certain
location
5 Shop-1-store HH Shopping, 1 store
6 Shop-n-store HH Shopping, multiple stores
7 Service-related
Task
activity
HH Renting movie, getting (fast) food, institutional
purposes (bank, post office, etc)
Social-independent P 8
Social-joint HH Meeting friends, relatives, etc
Leisure-independent P 9
Leisure-joint HH
Sports, café/bar, eating out, movie, museum,
library, etc.
Touring-independent P 10
Touring-joint
Non-task
activity
HH
Making a tour by car, bike, or foot (eg., letting out
the dog, etc)
Social, leisure and touring activities (see Table 5.1) are considered non-task
discretionary activities. As said, also task activities possibly can be done jointly.
However, as will become clear later, joint participation is a choice within a next
allocation decision.
As we are concerned with task activities and non-task activities, the models developed
in this study fit in the stage of the scheduling process when the work activity (if any),
business activities (if any) and transport mode used for the work activity have been
scheduled by both persons. In this stage, household decision making involves the
selection of task activities (bring/get, shop-1-store, shop-n-store, service) and joint non-
task activities (social-joint, leisure-joint, and touring-joint) and, furthermore,
determining which person conducts which task activity whereby conducting the
activity by both partners jointly is one of the options. Thus, note that joint participation
is a possible outcome for both task and non-task activities, but the processes are
different. In case of a task activity it is the result of two decisions, namely to include
the activity and next to conduct the activity jointly. On the other hand, in case of a non-
task activity it is the result of a one-step decision, namely to include a joint activity in
the schedule of both household heads. The selection and allocation decisions involved
in these steps will be the focus of this paper.
97
Timing of task and non-task activities takes place in the next stage. It defines the
duration and start time of activity categories both at the household level and person
level. Having defined the timing, trip-chaining choices are made. The last two
components include the car allocation and transport mode choice, particularly for each
non-work tour. The latter choices are conducted at either household or person level
depending on whether the tour includes a joint activity or not. It is noteworthy that,
each decision in this process model is modeled by a decision tree whereby the results
of earlier decisions are used as conditions for each next decision. Decisions made are
transformed in operations on an evolving schedule. The process results in a complete
schedule for each person.
5.3 MODELS SPECIFICATION
As mentioned earlier, this study focuses on activity-travel decisions between the heads
of household in activity participation and household task allocation. To give a better
interpretation for readers, we explain both models’ structure in this section.
5.3.1 Activity Selection
We propose the following process model for activity selection decisions including both
the task and non-task joint activities. Activities are considered sequentially based on a
pre-defined priority ordering of activity categories. A particular priority order is
assumed which corresponds to the order in which the activities are listed in Table 5.1.
For each activity category in order of priority, a same decision tree is used to decide
whether an activity of that category will be conducted under a set of relevant
conditions. If the answer is yes, then the next decision by the same decision tree
involves whether or not a second activity of the same category is to be selected. This is
repeated until the answer is negative. Then, the next activity category is considered
repeating the same process. The process continues until a negative decision is
generated for the last activity category. The activity selection decisions are handled by
a (single) decision tree, as developed and tested in the sections that follow. It is noted
that the action variable of the decision-tree model is a yes-no decision, namely whether
the activity considered will be added to the (evolving) schedule of the household or not.
Activity type is included in the decision tree as a condition variable given the notion
that the type of activity will have an influence on this decision (e.g., activities with
higher normal frequency will have a higher a-priori probability of being selected).
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5.3.2 Activity Allocation
Task activities added to the schedule are subject to a next allocation decision
determining who will do the task. The choice options are male, female, or both jointly.
Task activities are processed in the same order as before. In descending order of
priority, the activity categories are arranged as bring/get person, shopping to 1 store
(shop-1-store), shopping to multiple stores (shop-n-store) and service-related activities.
A single decision tree (to be developed below) will be used to make the allocation
decisions. In this decision-tree model, the condition variables used are the same as in
case of activity participation decisions, except that the number of activities in each task
and non-task activity category is used as additional condition variables.
5.4 DATA
The data used for deriving the decision trees originates from the Dutch National Travel
Survey (MON = Mobiliteit Onderzoek Netherlands) collected in 2004 covering all of
the Netherlands. The survey is conducted on a regular basis to obtain travel and activity
information of residents in the Netherlands. It is a household survey where data is
collected of all household members for the diary day as well as general information
about household and individual attributes such as, gender, age, vehicle ownership and
driving license ownership, home location, individual income, occupation, number of
working hours per week, etc. Respondents were also requested to give information
about all trips made on a designated day as well as on the activities conducted on trip
destinations. Information for each trip includes start time, trip purpose, destination,
activity type at the destination, and transport mode. Situational variables are reported
as well. All in all, this survey provides a comprehensive data source to analyze
activity-travel behavior of Dutch residents. In the data collection, 29,221 households
filled out a one-day travel/activity diary and 28,600 of these households fit the criteria
for being considered in ALBATROSS. The data were transformed to an activity-diary
data format for the current estimation purpose. In this study, we focus on two-heads
household, i.e. households consisting of a single head are not included in the analysis.
Then, there are 18,037 households used for deriving the envisioned decision tree.
5.5 ANALYSES
5.5.1 Decision Tree Induction
We applied a CHAID-based tree induction method to identify the rules that describe
which choices (i.e., actions) are made under which conditions. CHAID (Chi-square
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Automatic Interaction Detector) generates non-binary trees, i.e., trees where more than
two branches can be attached to a single root or node, based on a relatively simple
algorithm that is particularly well suited for the analysis of larger datasets. Other
decision tree induction systems are C4.5 (Quinlan, 1993) and CART (Breiman et al,
1984). CHAID relies on the Chi-square test to determine the best next split at each step.
CHAID generates a decision tree by splitting subsets of the space into two or more
nodes repeatedly, beginning with the entire data set (Kass, 1980). The split that
maximizes a significance value of a Chi-square test, after adjustment for multiple tests
(Bonferroni adjustment), across condition variables is used for splitting if the split is
significant. The process is repeated for each newly created group until no more
significant splits are found. This process of extracting the rules is the same as the one
used in the original ALBATROSS model. In order to develop the decision tree, 75% of
the cases are used for training and the remaining cases were used for validation.
Generally, in deriving ALBATROSS decision models, the observed choice and
attributes of the household, person, space-time setting and schedule as far as known in
the stage considered of the assumed decision process are used as conditions and
extracted from the diary data in addition to the choice outcome (the target or action
variable).
The CHAID decision tree induction method allows one to define the threshold for
splitting in terms of a significance level for the Chi-square ( 2χ ) measure and a
minimum number of cases at leaf nodes. Alpha was set to 5% and the minimum
number of cases at leaf nodes to 50 (model 1) and 75 (model 2). As a measure of
prediction accuracy, the expected hit ratio is calculated. ALBATROSS uses a
probabilistic action-assignment rule and therefore the hit-ratio measure used here
represents the expected proportion of cases predicted correctly when a probabilistic
response-assignment rule is used. It is calculated as:2( )1 kq
kqk
f
N N∑ where fkq is the
frequency of the qth action at the kth leaf node, N is the total number of cases and Nk is
the number of cases at the k-th leaf node. Note that the expected hit ratio is comparable
to a likelihood measure and, generally, yields lower scores than the deterministic
counterpart of the measure.
5.5.2 Deriving Impact Tables
Decision trees derived from data may become very large and complex and,
consequently, be difficult to interpret. This holds true particularly for the present
application where the number of choice observations is very large. Arentze and
Timmermans (2003) developed a method to derive elasticity information from rule-
100
based models to facilitate interpretation, which we will use here to describe the results
of tree induction. The principle of the proposed method is straightforward. After
having derived a rule-based model from training data, the model is used to predict for
each condition variable a frequency cross table with the levels of the condition
variables in rows and the the levels of the target variable (i.e., the action variable) in
columns. The frequency table is generated by applying the model as many times as
there are levels of the condition variable. In each run, each training case is assumed to
take on the level considered on the condition variable. Thus, the results of a run
indicates the predicted frequency distribution for the action variable assuming that each
training case has the same determined level of the condition variable. Then, the impact
of the condition variable is measured as the Chi-square for the frequency table.
Formally:
( )s sIS D= F [5.1]
where D is a Chi-square measure of the frequency table generated (Fs) for condition
variable s. This measure can be decomposed into a measure of impact on each level of
the action variable, as follows:
( )si siIS D= F [5.2]
where again D is a chi-square measure and Fsi is the vector of predicted frequencies of
the i-the action under the levels of the s-th condition variable. Apart from impact size,
we also use a measure of the direction of impact proposed by Arentze and
Timmermans (2003) defined as:
( ), 12
, 12
ij i jj
si
ij i jj
f fMS
f f
−=
−=
−=
−
∑∑
[5.3]
where fij is the predicted frequency of action i under the j-th level of condition variable
s. This measure can be interpreted as a measure of monotonicity. If the condition
variable has a monotonically increasing impact on the frequency of action i across the
levels of the condition variable, then MSsi equals 1 and if it has a monotonically
decreasing impact it equals -1. Any value in between these extremes indicates that the
impact is non-monotonous in the direction indicated by the sign across the range of the
condition variable.
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5.5.3 Condition and Action Variables
Table 5.2 portrays the condition variables that were used as input to the algorithm for
both decision trees. The condition variables concern household level (including
accessibility indicators), individual level, and activity level variables. Note that in this
stage of the scheduling process, work, business, and other mandatory activities are
known. However, only condition information related to the work activity is fully used
for condition variables, such as number of work activities, duration of each work
activity, mode to work place, and total time engaged in work activity. Continuous
condition variables, such as duration, are discretisized by using an equal-frequency
interval method which divides a continuous variable into n parts, in which each part
contains approximately the same number of cases.
It is worth noting that some variables are related to household level and person level, while others are defined at schedule level and activity level. Age of the youngest child
in a household is considered as a condition variable as well as other household
attributes, such as urban density (of the residence location), household composition,
day of the week, and socio-economic class (#1-5 in Table 3). Given the selection of
two-heads households, household composition refers to only three household types:
double-one-worker, double-two-workers and double-no-workers. The number of cars
in the household is also included as a household variable (# 21). A final set of
household-level variables relates to measures of accessibility of locations given the
home location of the household. On this level, 8 variables (#10-17) are included: (1)
daily goods sector: number of employees within 3.1 km, (2) non-daily goods sector:
number of employees within 4.4 km, (3) all sectors: number of employees within 4.4
km, (4) size of population within 3.1 km, (5) daily goods sector: distance within which
160 employees work, (6) non-daily goods sector: distance within which 260 employees
work, (7) all sectors: distance within which 4500 employees work, and (8) distance
within which 5200 people live.
Note that attributes related to individuals can be incorporated only if they are specified
explicitly for the male-female heads. Thus, individual attributes will be tied together
with gender. Individual attributes such as work status and age are explained in #6-9 in
Table 3. The work status attribute of the male-female heads indicates whether the
person has no work, part-time work, or full-time work. Those who work more than 32
hours per week are considered as full-time worker. Age and possession of driving
license by the householders are also one of the individual attributes (#8-9 & #22-23).
The following attributes are defined on the schedule level. The number of work
activities conducted by male and female is known and we limit it to maximally 2 work
activities per person (#18-20) (only very few diaries where more than two work
activities are included).
102
TABLE 5.2 Condition Variables for Decision Tree Models
No Variable Classification Acronym Category
1 Urban Density 0=most densely , 4= least densely1,2 Urban Ordinal
2 Household Composition 2=2 heads, 1 worker, 3=2 heads, 2 workers, 4=2 heads, no
workers1,2
Comp Nominal
3 Youngest children in HH 0=no children, 1=<6, 2=6-11, 3=12-17 yr1,2 Child Nominal
4 Day of the week 0=Monday to 6=Sunday1,2 Day Nominal
5 Socio-economic class 0=low, 1=low-mid, 2=mid-high, 3=high1,2 SEC Ordinal
6 Working status – M 0= non-worker, 1= part-time, 2= full-time1,2 WstatM Nominal
7 Working status – F 0= non-worker, 1= part-time, 2= full-time1,2 WstatF Nominal
8 Age of person – M 0=<35, 1=35-<55, 2= 55-<65, 3= 65-<75, 4= 75+ years1,2 AgeM Ordinal
9 Age of person – F 0=<35, 1=35-<55, 2= 55-<65, 3= 65-<75, 4= 75+ years1,2 AgeF Ordinal
10 Accessibility – 1 0=<=115, 1=<=253, 2=<=307, 3=<=507, 4=<=675, 5=>6751,2 nEmp1 Ordinal
11 Accessibility – 2 0=<=395, 1=<=635, 2=<=762, 3=<=938, 4=<=2525, 5=>25251,2 nEmp2 Ordinal
12 Accessibility – 3 0=<=8785, 1=<=12995, 2=<=16120, 3=<=20199, 4=<=70314,
5=>703141,2
nEmp3
Ordinal
13 Accessibility – 4 0=<=5050, 1=<=8845, 2=<=13217, 3=<=16833, 4=<=22884,
5=>228841,2
SizePop
Ordinal
14 Accessibility – 5 0=<=71, 1=<=127, 2=<=165, 3=<=202, 4=<=346, 5=>3461,2 Dist1 Ordinal
15 Accessibility – 6 0=<=92, 1=<=145, 2=<=176, 3=<=258, 4=<=334, 5=>3341,2 Dist2 Ordinal
16 Accessibility – 7 0=<=92, 1=<=128, 2=<=201, 3=<=274, 4=<=360, 5=>3601,2 Dist3 Ordinal
17 Accessibility - 8 0=<=0, 1=<=105, 2=<=126, 3=<=163, 4=<=278, 5=>2781,2 Dist4 Ordinal
18 Number of work episodes – M 0=no work, 1=1 ep, 2=2 ep1,2 NworkM Ordinal
19 Number of work episodes – F 0=no work, 1=1 ep, 2=2 ep1,2 NworkF Ordinal
20 Number of work episodes – HH 0=no work, 1=1 ep, 2=2 ep, 3=3 ep, 4=4 ep1,2 NworkHH Ordinal
21 Number of cars in HH 0, 1, 2+1,2 Ncar Ordinal
22 Driving license possession – M 0= no, 1= yes1,2 DrivM Nominal
23 Driving license possession – F 0= no, 1= yes1,2 DrivF Nominal
0=0; 1=≤435; 2=436-544; 3=545-575; 4=>5751 24 Duration of work act – M (min)
0=0; 1=≤390; 2=391-540; 3=541-570; 4=>5702 DurM Ordinal
0=0; 1=≤297.25; 2=297.26-480; 3=481-555; 4=>5551 25 Duration of work act – F (min)
0=0; 1=≤250; 2=251-385; 3=386-540; 4=>5402 DurF Ordinal
0=0; 1=≤475; 2=476-566; 3=567-820; 4=>8201 26 Duration of work act in HH (min)
0=0; 1=≤415; 2=416-550; 3=551-655; 4=>6552 DurHH Ordinal
27 Mode to work place – M 0= non-work act, 1=PT, 2=CP, 3=CD, 4=S 1,2 ModeM Nominal
28 Mode to work place – F 0= non-work act, 1=PT, 2=CP, 3=CD, 4=S 1,2 ModeF Nominal
29 Mode to work place – HH 0= non-work act, 1=PT, 2=CP, 3=CD, 4=S 1,2 ModeHH Nominal
30 –
35
Time available for non-work act
(1-6) – M (min) 0=0, 1=<=30, 2=30-60, 3=60-90, 4=90-1201,2
Time1M-
Time6M
Ordinal
36 –
41
Time available for non-work act
(1-6) – F (min) 0=0, 1=<=30, 2=30-60, 3=60-90, 4=90-1201,2
Time1F-
Time6F
Ordinal
42 –
47
Time available to use car for non-
work act in HH (1-6) 0=0, 1=<=30, 2=30-60, 3=60-90, 4=90-1201,2
Time1C-
Time6C
Ordinal
48 Given condition of work act – M 0= no, 1= yes1,2 yWorkM Nominal
49 Given condition of business act – M 0= no, 1= yes1,2 yBusiM Nominal
50 Given condition of other act – M 0= no, 1= yes1,2 yOthM Nominal
51 Given condition of work act – F 0= no, 1= yes1,2 yWorkF Nominal
52 Given condition of business act – F 0= no, 1= yes1,2 yBusiF Nominal
53 Given condition of other act – F 0= no, 1= yes1,2 yOthF Nominal
54 # activities 1 currently done in HH 0=0, 1=1, 2=2, 3=3, 4=4, 5=5+ 1,2 nbr Ordinal
55 # activities 2 currently done in HH 0=0, 1=1, 2=2, 3=3, 4=4, 5=5+ 1,2 nsh1 Ordinal
56 # activities 3 currently done in HH 0=0, 1=1, 2=2, 3=3+ 1,2 nshn Ordinal
57 # activities 4 currently done in HH 0=0, 1=1, 2=2, 3=3+ 1,2 nser Ordinal
58 # activities 5 currently done in HH 0=0, 1=1, 2=2+ 1 nsoc Ordinal
59 # activities 6 currently done in HH 0=0, 1=1, 2=2+ 1 nlei Ordinal
60 # activities 7 currently done in HH 0=0, 1=1, 2=2+ 1 ntou Ordinal
1=bring/get, 2=shop-1-store, 3=shop-n-store, 4=service,
5=social-joint, 6=leisure-joint, 7=touring-joint1 61 HH activity type considered 1=bring/get, 2=shop-1-store, 3=shop-n-store, 4=service 2
HHact Nominal
Note: M=Male; F=Female; ep=episode; PT=Public Transport; CP=Car Passenger; CD=Car Driver; S=Slow
1 Alternative classification used for activity participation model (model 1)
2 Alternative classification used for household task allocation model (model 2)
103
Duration of work activity conducted by male or female and the total work duration
across male-female in a household are also used as condition variable. Transport mode
used to the work place by male or female worker is also used as condition variable
(#27 & 28). In case multiple modes are involved, we aggregated the mode by arranging
modes in a hierarchy as follows: (1) Public Transport (2) Car Passenger (3) Car Driver
and (4) Slow (Bike and Walk) as can be seen in #29.
Additionally, available time in the schedule is captured. In order to identify how much
time is available for doing activities other than work activity at different times of the
day for male and female, we segment the period from 8 am to 8 pm into 6 time spans
of 2 hours. For each 2 hour-period we calculated the available time in the schedule and
classified this time into 5 categories, where zero means no time left for doing a non-
work activity and the remaining categories identify how much time is left for doing a
non-work activity in multitudes of half an hour (#30-41). In addition to available time,
the time a car is available for a non-work activity is also considered as a condition
variable. Hereby, the number of cars available and the transport mode(s) used for work
activities, if any, of the two household heads are taken into account. Time is
segmented in the same way as above (Note: zero means that either car is not available
due to work activities or because no car is available in the household).
Variables #48-53 indicate whether work, business and other mandatory activities (such
as go to school) are conducted on the given day or not. Note that, these variables are
also schedule-level variables. The remaining variables (#54-60) are activity level
variables. These variables indicate for each task and joint non-task activity category the
number of activities that, at the moment of the decision, are included in the schedule as
a consequence of previous activity selection decisions. Thus, these variables are
dynamic and depend on the assumed priority order of activities. At the time of the first
decision, no activity is included in the schedule. Therefore, all these variables (#54-60)
are zero in the beginning. For a second decision, the result of the first decision is
known and if this implied the insertion of a (bring-get) activity the corresponding
variable has a value of one, and so on. In sum, this set of variables indicates for each
current decision the current schedule state as a result of previous activity selection
decisions. Note that, for household task allocation model, only number 54-57 are being
concerned. The last variable encodes the activity type that is considered in the current
selection decision. This variable has seven levels corresponding to the seven activity
categories (task and joint non-task activities) considered in the process model.
Consequently, only four levels variables for the allocation model.
As a result, a total of 61 condition variables were defined for the activity participation
model as indicated by superscript 1 in Table 5.2. For household task allocation model,
104
the condition variables are actually almost the same as that we used for activity
participation model. Nevertheless, different classifications were used for some
variables, such as work duration, number of instances of a particular activity at the
moment the decision is made, and obviously the type of the activity considered, as
indicated by superscript 2 in Table 5.2. Hence, 58 condition variables remain for
household task allocation model.
5.5.4 Results: Activity Participation Tree
For deriving the activity participation model, a total of 153,856 observations could be
derived from the data set. 75% of these cases (115,458) were used for training and the
remaining cases were used for validation. Of 153,856 cases, the probability of
observing a yes decision for each activity category is as follows: bring/get person
20.2%, shop-1-store 46.5%, shop-n-store 9.1%, service 10.4%, social-joint 6.4%,
leisure-joint 4.5% and touring-joint 3.0%.
The tree generated by CHAID consists of 386 leaf nodes (decision rules). The hit ratio
(based on a probabilistic assignment rule) of the model compared to a null-model (a
root-only decision tree) indicates a modest but significant improvement: the hit-ratio of
a null-model equals 0.697 and the hit ratio of the tree after splitting equals 0.777. A
Chi-square-based contingency coefficient of 0.455 confirms that there is a moderately
strong impact of the decision tree structure on the action variable. The overall accuracy
on the validation set is almost the same, dropped slightly from 0.777 to 0.772.
Due to limited space and given the large number of decision rules, we cannot display
the entire results of the decision tree. Instead, in order to give a summary view of the
outcome, we will discuss the results of the impact analysis in terms of the IS and MS
measures explained above.
Table 5.3 displays the impact table for the activity participation model. In this case, the
choice variable is a binary variable (yes/no decision), so that the MS measures are
perfectly correlated (MSyes = − MSno). As it appears, activity type (HHact) is by far the
most important variable for the activity selection decision. The monotonicity index MS
in this case is close to zero (+/- 0.21) and negative for the yes decision indicating that
the frequency of adding an activity decreases across the activity categories in the order
they are put, but not monotonically. The second most important variable is day of the
week (Day). There is a tendency (MSyes = -0.23) of decreasing probability of adding an
activity of the activity category concerned to the schedule with increasing values of this
variable (running from Monday to Sunday).
105
TABLE 5.3 Impact of Condition Variables of HH Activity Participation Model
No Variables IS ISyes ISno MSyes MSno
1 HHact 107437 91962.17 15452.71 -0.21 0.21
2 Day 4168.91 3390.36 778.64 -0.23 0.23
3 nbr 2053.11 1616.28 436.82 0.23 -0.23
4 Child 1986.13 1601.17 384.98 0.05 -0.05
5 nsh1 1051.52 864.75 186.81 -0.49 0.49
6 nser 790.11 629.86 160.25 0.52 -0.52
7 nshn 501.63 405.30 96.39 0.29 -0.29
8 DurHH 499.02 406.40 92.64 -1.00 1.00
9 ModeF 273.66 221.14 52.53 -0.07 0.07
10 ModeHH 75.3 61.26 13.91 -0.25 0.24
11 Dist1 54.04 43.99 10.05 0.34 -0.33
12 SEC 48.87 39.61 9.23 1.00 -1.00
13 Time3M 26.23 21.22 5.01 0.81 -0.81
14 Time4F 23.57 19.14 4.38 1.00 -1.00
15 DurF 22.29 18.10 4.13 -1.00 1.00
16 ntou 20.8 16.79 4.00 1.00 -1.00
17 NworkHH 15.22 12.47 2.74 -1.00 1.00
18 AgeF 13.02 10.60 2.44 0.12 -0.12
19 yBusiM 12.69 10.37 2.32 -1.00 1.00
20 DrivF 11.34 9.30 2.06 1.00 -1.00
21 yWorkM 8.73 7.13 1.58 -1.00 1.00
22 NworkF 7.59 6.14 1.44 -0.15 0.14
23 AgeM 7.43 6.05 1.35 -1.00 1.00
24 Ncar 6.00 4.81 1.17 0.56 -0.57
25 yOthM 5.76 4.70 1.08 1.00 -1.00
26 Dist2 5.5 4.41 1.03 -0.04 0.02
27 DrivM 4.99 4.07 0.92 1.00 -1.00
28 ModeM 4.68 3.80 0.86 -1.00 1.00
29 Dist3 4.40 3.50 0.83 0.05 -0.06
30 Urban 4.31 3.54 0.81 0.06 -0.05
31 nlei 3.58 2.88 0.67 1.00 -1.00
32 nEmp2 3.52 2.81 0.64 -0.15 0.15
33 DurM 3.39 2.79 0.64 -1.00 1.00
34 Time1F 2.93 2.38 0.55 -0.25 0.25
35 SizePop 2.64 2.09 0.48 0.85 -0.85
36 Time4M 2.48 2.02 0.47 1.00 -1.00
37 NworkM 2.31 1.86 0.43 0.28 -0.29
38 WstatM 1.76 1.43 0.35 -1.00 1.00
39 Comp 1.65 1.40 0.29 0.38 -0.38
40 Time2F 1.56 1.24 0.27 1.00 -1.00
41 Time3F 1.47 1.20 0.28 0.63 -0.66
42 nEmp1 1.32 1.17 0.26 -0.07 0.07
43 Dist4 1.21 0.94 0.21 -1.00 1.00
44 nsoc 1.11 0.92 0.22 1.00 -1.00
45 Time3C 0.92 0.74 0.18 1.00 -1.00
46 Time1M 0.73 0.59 0.14 -0.02 0.03
47 Time4C 0.46 0.41 0.09 -1.00 1.00
48 Time5C 0.37 0.28 0.06 0.23 -0.23
49 nEmp3 0.33 0.28 0.07 1.00 -1.00
50 WstatF 0.17 0.17 0.04 -0.79 0.75
51 yWorkF 0.11 0.09 0.02 -1.00 1.00
52 Time5M 0.09 0.07 0.01 0.42 -0.39
106
The next most influential variable is the number of bring/get activities already included
in the schedule at the moment of the decision (nbr). Given a positive sign of MS for the
yes decision (MSyes = 0.23), there is a tendency of bring/get activities to generate other
activities. However, given that MS is smaller than 1, it does not monotonically increase,
at some point the probability of adding activities decreases with increasing number of
bring/get activities in the current schedule.
In terms of socio-demographic variables, in order of decreasing importance, we find
that presence/age of young children in the household (Child), income (SEC), age of
female head (ageF), female has a driving license (DrivF), age of male head (ageM),
number of cars (Ncar), male has a driving license (DrivM), work status of male
(WstatM), household composition (Comp), and work status of female (WstatF) all have
an influence.
As for the variable Child the MS index indicates that with increasing level of this
variable, the frequency of household activities is not increasing monotonically (MS =
0.05) across the levels (ordered as no children and children of increasing age group).
Interestingly, the MS measure for the variable that gives the second biggest impact,
SEC, indicates that as income goes up, the frequency of household activities
monotonically increases (MS = 1). Having a driving license also increases the number
of household activities as indicated by the positive sign (MS = 1) for DrivM and DrivF
variables.
In terms of the work-related condition variables, total duration of work activity across
the household heads (DurHH), mode to work by female (ModeF) and mode to work by
the two heads (ModeHH) turn out to be the most significant variables on this level.
With increasing total work duration (DurHH) the frequency of household activities
decreases monotonically, as one would expect (MS = -1). On the other hand, with
increasing values of mode (ordered as Public Transport, Car Passenger, Car Driver and
Slow mode) either by female or aggregated across the two householders the frequency
of household activities does not increase but rather shows a tendency to decrease (MS
= -0.07 and MS = -0.25).
The variables related to available time for non-work activities show influences on
household activity selection choices in expected directions. In particular, time available
during 12 am - 2 pm for male (Time3M) and during 2 - 4 pm for female (Time4F) have
almost monotonically increasing impacts on the activity frequencies (MS = 0.81 and
MS = 1.00). In terms of the accessibility of locations, the number of employees within
3.1 km in the daily good sector (Dist1) turns out to be the most influential variable.
With increasing number of employees the frequency of household activities increases,
107
although not monotonically (MS = 0.34). In summary, almost all of the 61 condition
variables used as input to the induction process recur in the decision tree. Only 9
variables do not affect predictions of activity participation, as indicated by zero value
of the chi-square-based impact measure.
5.5.5 Results: Task Allocation Tree
Having discussed the activity participation tree model, we now turn to the derivation of
the task allocation model. In total 22,512 observations could be derived from the data
set. To develop the decision tree, again 75% of these cases (16,893) were used for
training and the remaining cases were used for validation. Of 16,893 cases, the
probability of observing task allocation decisions for each household task activity
category is as follows: bring/get person 25%, shop-1-store 52.3%, shop-n-store 9.7%,
and service 13%. Overall, the probabilities of observing each person category are as
follows: male 31.8%, female 57%, and both 11.2%.
The tree generated by CHAID consists of 94 leaf nodes (decision rules). The hit ratio
(based on a probabilistic assignment rule) of the model, compared to a null-model (a
root-only decision tree) indicates a modest but significant improvement: the hit-ratio of
a null-model equals 0.439 and the hit ratio of the tree after splitting equals 0.545. A
Chi-square-based contingency coefficient of 0.504 confirms that there is a moderately
strong impact of the decision tree structure on the action variable. The overall accuracy
on the validation set is almost the same, dropped slightly from 0.545 to 0.537. Again,
space limitation does not allow us to discuss the structure of the decision tree. To give
a summary view of the result, we will discuss the results of the impact analysis in
terms of the IS and MS measures.
Table 5.4 shows the impact table for the task-allocation model. In this model, the
choice variable is a non-binary variable. Work duration of male (durM) appears to be
the most important variable for the allocation decision. There is a tendency of
increasing probability of female to do household tasks as male’s work duration
increases, although not monotonically (MSfemale = 0.88).
Over the same range, the probability of both (jointly) tends to decrease (MSboth = -0.78).
The second most important variable is the number of bring/get activities that are
included in the schedule of the household as the result of the previous participation
decisions (nbr). The probability that the female conducts a task activity increases
monotonically with increasing number of bring/get activities in the current schedule
(MSfemale = 1.00).
108
TABLE 5.4 Impact of Condition Variables of Task-Activity Allocation Model
No Variables IS ISmale ISfemale ISboth MSmale MSfemale MSboth
1 durM 9887.64 3615.91 3100.84 3170.67 -0.79 0.88 -0.78
2 nbr 7867.01 1799.28 1630.63 4437.18 -1.00 1.00 -1.00
3 nsh1 6188.85 562.57 1123.68 4502.61 -0.94 1.00 -1.00
4 durF 4324.79 2209.56 1235.52 879.65 1.00 -0.87 -0.52
5 yBusiM 904.81 100.46 250.61 553.74 -1.00 1.00 -1.00
6 nshn 562.63 9.47 97.49 455.70 -1.00 1.00 -1.00
7 nser 238.56 0.14 33.39 205.04 -1.00 1.00 -1.00
8 HHact 187.19 90.37 12.62 84.18 -0.27 0.67 0.05
9 time1F 97.84 57.56 15.58 24.69 -1.00 1.00 0.98
10 wstatF 64.95 44.63 18.04 2.27 -0.92 0.91 1.00
11 time4M 35.73 18.25 15.16 2.32 0.63 -0.62 0.56
12 durHH 32.42 21.78 7.96 2.67 1.00 -1.00 -1.00
13 Child 30.23 6.19 11.55 12.48 -0.34 0.37 -0.40
14 SEC 21.47 8.00 0.99 12.48 0.14 0.28 -0.52
15 Urb 13.11 0.11 1.94 11.07 0.81 1.00 -1.00
16 SizePop 7.08 1.00 0.18 5.91 -0.45 -0.80 1.00
17 AgeM 4.98 0.00 0.79 4.18 -1.00 -1.00 1.00
18 nEmp2 3.59 2.37 1.12 0.09 1.00 -1.00 -0.57
19 wstatM 3.38 1.70 1.43 0.25 1.00 -1.00 1.00
20 Ncar 3.11 0.47 0.50 2.13 -1.00 0.01 0.55
21 yWorkF 2.83 1.85 0.97 0.01 1.00 -1.00 -1.00
22 time5M 2.07 0.90 0.90 0.29 1.00 -1.00 1.00
23 Day 1.67 0.56 0.01 1.10 0.00 0.00 0.00
24 Dist1 1.21 0.49 0.51 0.20 -1.00 1.00 -1.00
25 drivF 0.76 0.02 0.19 0.55 -1.00 1.00 -1.00
26 time1C 0.52 0.30 0.21 0.01 -1.00 1.00 -1.00
27 modeF 0.5 0.32 0.10 0.06 0.00 0.00 0.00
28 time4F 0.4 0.24 0.15 0.00 -1.00 1.00 -1.00
29 AgeF 0.32 0.17 0.13 0.02 -1.00 1.00 -1.00
30 Dist3 0.32 0.21 0.07 0.03 0.61 -0.53 -1.00
31 Comp 0.14 0.05 0.06 0.02 1.00 -1.00 1.00
The same holds for number of 1-store shopping activities (nsh1), number of n-store
shopping activities (nshn) and number of service activities (nser) in the current
schedule. These results suggest that female tends to take on task activities particularly
when multiple tasks are scheduled. The next most influential variable is the work
duration of female (durF). The tendency of female getting a household task decreases
when her work duration increases, whereas the probability of the male doing a
household task increases monotonically (MSmale = 1.00). The last variable in the top 5
most significant variables is presence of a business activity in the male’s schedule
(yBusiM). The probability that the female takes on the household task increases when
the male has a business activity in the schedule. In contrast to the activity participation
tree, activity type (HHAct) is not the most important variable in the allocation tree.
109
Nevertheless, the probability of the female doing the household task increases non-
monotonically (MSfemale = 0.67) when the level of activity category rises in the order of
bring/get, shop-1-store, shop-n-store, service related.
In terms of socio-demographic variables, in order of decreasing importance, we find
that work status of female (WstatF), presence of young children in the household
(Child), income (SEC), age of male head (ageM), work status of male (WstatM),
number of cars (Ncar), female has a driving license (DrivF), age of female head (ageF),
and household composition (Comp) all have an influence. An increase of male’s or
female’s work status (WstatM and WstatF) tends to increase the probability of task
allocation to both of the heads monotonically (MSboth = 1.00). As for the presence of
young children in the household, Child, the MS index indicates that with increasing
level of this variable, the probability of female takes over the household task increases
non-monotonically (MS = 0.34) across the levels. Interestingly, the MS measure for the
variable of income level, SEC, indicates that as income goes up, the probability of male
and female jointly doing the household task decreases non-monotonically (MSboth = -
0.52). In terms of a person’s age, increasing age of the male tends to increase the
probability of both heads to conduct household tasks together (MSboth = 1.00).
However, increasing age of the female only seems to increase the female’s probability
to do the household tasks (MSfemale = 1.00). An increase of number of cars in the
household, Ncar, tends to decrease the male’s probability to conduct the household
task (MSmale = -1.00). Having a driving license increases the female’s probability to
perform the household tasks as indicated by the positive sign (MSfemale = 1.00) for
DrivF variable. Finally, as the level of household composition rises (Double-1-worker,
Double-2-workers, and Double-no-workers), the tendency of male to do household
tasks increases monotonically (MSmale = 1.00).
5.6 CONCLUSIONS AND DISCUSSION
This study was intended to refine the ALBATROSS model. In the present paper, we
focus on activity participation choice of male-female heads, in particular those
activities that are related to a household task or that are conducted jointly, in order to
capture within-household interactions in a better way.
For household activity selection and task allocation decisions rule-based models using
a CHAID-based algorithm were derived from activity-trip diary data. The activity
participation model, given the large number of observations that could be derived from
the data, included more than 300 condition-action rules. Although slightly smaller, the
household task allocation model also involved an extensive set of decision rules,
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involving more than 90 condition-action rules. In both cases, the validity of the
decision tree is satisfactory in the sense that the derived rules are readily interpretable
and the overall goodness-of-fit of the model on a validation set is acceptable as well.
Furthermore, in both cases, a substantial improvement in goodness-of-fit relative to a
null model indicates that there is a moderately strong association between condition
variables at household, individual, activity and schedule level, on the one hand, and the
participation and allocation decisions, on the other. Furthermore, the stability of
performance on a validation set suggests that derived rules are generalizable to unseen
cases. These results suggest that the way of structuring the household decisions as we
proposed in this study has merits.
To aid interpretation of the complex decision trees, the method of impact tables
(Arentze and Timmermans 2003) was used to measure the size and sign of the impact
of each condition variable on predictions. As expected, the most important variable in
household activity participation model is activity type, followed by day of the week
and number of bring/get activities already included in the schedule by the time of
making the activity participation decision. The presence of young children in the
household and income are also relatively influential variables among the socio-
demographic variables considered in the model. In terms of time-availability variables,
the total duration of work activity across household heads appears to be the most
influential variable. Accessibility variables only have modest impacts. Accessibility of
facilities in the daily goods sector appears to be the most important variable among the
accessibility variables. However, in general, the accessibility variables only have
modest impact on the frequency of doing household activities. This suggests that
spatial developments or policies will only have a slight influence on this aspect of
household behavior. On the other hand, a significant impact of income level suggests
that economic growth has larger impact on the behavior.
In the household task allocation model, the most important variable is male’s work
duration. Although not as big as the male’s influence, female’s work duration also has
an influence. Work status of female turns out to be the most significant variable among
the socio-demographic variables.
By refining the existing ALBATROSS in this way we expect that the accuracy and
sensitivity of predictions will be improved. The focus of the present study is on the
estimation of the model and the results of the model-based analysis for those particular
choice facets. Since the structure of the activity scheduling process differs, the models
cannot be compared on a single-choice facet basis. Only on the level of activity
patterns that are the result of a full activity scheduling process the two models could be
compared. This comparison is left as a topic of future research.
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Allocation Decisions in Automobile Deficient Households”. In: Proceedings ETC 2007 Conference, Noordwijk, The Netherlands.
Arentze, T.A. and Timmermans, H.J.P. (2000), ALBATROSS: A Learning-based Transportation Oriented Simulation System. EIRASS, Eindhoven University of
Technology, The Netherlands.
Arentze, T.A., and Timmermans, H.J.P. (2003), “Measuring Impacts of Condition
Variables in Rule-Based Models of Space-Time Choice Behavior: Method and
Empirical Illustration”. Geographical Analysis, 35, 24-45.
Arentze, T.A. and Timmermans, H.J.P. (2004), “A Learning-based Transportation
Oriented Simulation System”. Transportation Research Part B, 38, pp.613-633.
Arentze, T.A. and Timmermans, H.J.P. (2005), ALBATROSS 2.0: A Learning-based Transportation Oriented Simulation System. EIRASS, Eindhoven University of
Technology, The Netherlands.
Breiman L., Friedman, J.J., Olshen, R.A., and Stone, C.J. (1984), Classification and Regression Trees. Wadsworth, Belmont [CA].
Gliebe, J.P. and Koppelman, F.S. (2002), ”A Model of Joint Activity Participation
between Household Members”. Transportation, 29, pp.49-72.
Gliebe, J.P. and Koppelman, F.S. (2005), “Modeling Household Activity-Travel
Interactions as Parallel Constrained Choices”. Transportation, 32, pp.449-471.
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Categorical Data”. Applied Statistics 29, 119-27.
Miller, E.J., and M.J. Roorda. (2003), “A Prototype Model of Household
Activity/Travel Scheduling”. Proceedings of the 2003 Transportation Research Board, Washington DC, USA.
Quinlan, J.R. (1993). C4.5 Programs for Machine Learning. San Mateo, Calif.:
Morgan Kaufmann Publishers.
Scott, D. and Kanaroglou, P. (2002), “An Activity-episode Generation Model that
Captures Interaction between Household Heads: Development and Empirical
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Shopping Activities in a Household”. Proceedings of the 2004 Transportation Research Board, Washington, DC.
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Chapter 6
CONTINUOUS CHOICE MODEL OF TIMING AND
DURATION OF JOINT ACTIVITIES
Anggraini, R., Arentze, T.A., and Timmermans, H.J.P., 2009. Paper is accepted for
publication at Transportation Research Record, Journal of Transportation Research Board, USA In press.
ABSTRACT This paper contributes to the recent interest in household decisions in activity-based analysis. It focuses on the joint participation of male-female heads in non-work activities and attempts to model the timing and duration decisions for these activities, using decision tree induction. The data used originate from the 2004 National Travel Survey in the Netherlands. The results show that activity type has the most significant influence in both models. In addition, time availability for non-work activities during morning off-peak periods has a strong influence on start time decisions. The results also suggest that there is a substantial influence of duration decisions on start time decisions. Joint participation of household members in activities tends to lead to longer activity duration and earlier start times. Overall, modeling timing and duration of joint activity participation decisions at the household level proves to have some clear advantages.
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6.1 INTRODUCTION
Interactions between persons in the household will strongly influence individuals’
activity-travel patterns. Joint activity participation requires the synchronization of the
activity patterns of the persons involved (Golob and McNally, 1997; Gliebe and
Koppelman, 2002; and Bhat and Pendhyala, 2005). In addition, understanding
relationships among different persons and their underlying motivations for activity
participation can also help to understand the potential impacts of policy triggers to
change travel behavior. All this explains a recent surge in research papers and
forecasting models that attempt to explicitly account for within-household interactions
in activity participation and travel (Goulias, 2000).
In the ALBATROSS system (Arentze and Timmermans, 2000, 2004, 2005), on which
we focus in this study, within household interactions are incorporated in the sense that
the process-wise sequential formation of an individual’s activity schedule does not only
take into account the previous schedule decisions of that individual but also those of
the spouse. Hence, agendas are formed sequentially and in parallel between the adults
in a household. Although this approach gives satisfying results, we decided to give the
model system an overhaul by looking more systematically at the modeling of the
various choice facets from the perspective of household decision making.
This paper reports the results of modeling duration and start time decisions for joint
activities. The results of this paper should not only be viewed as a step in refining the
process model of ALBATROSS but are also relevant in their own right. In the
ALBATROSS framework, duration and timing decisions for joint activities are
explicitly considered in the context of a broader activity scheduling decision process.
This means that only those choices that were already made in a previous scheduling
step can be considered to influence the duration and timing decisions for joint activities
at the moment they are made. The decision-process perspective also means that most
previous research on start time and activity duration is not immediately relevant for our
approach.
It may however be of interest to compare the ultimate results of the previous rich
literatures on departure time choice (e.g., Bhat and Steed, 2002, and Steed and Bhat,
2000), activity duration (e..g. Bhat, 2002, and Niemeier and Morita, 1996), and the
combination of timing and duration decisions (Pendhyala and Bhat, 2004, and Vovsha
and Bradley, 2004). Most studies addressed one particular activity purpose, such as
shopping or social activity, or one particular non-work group activity, such as
maintenance or leisure activities. Moreover, existing studies typically focused on
individual decision making and ignored the broader context of a daily activity schedule.
There are a few notable exceptions. Vovsha and Bradley (2004) considered the
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relationship between departure time and duration in the context of an activity-based
model of activity travel demand. They considered independent and joint activities in
the context of travel tours. Habib et. al. (2008) also acknowledging activity scheduling
behavior emphasized the social context of activity scheduling decisions. They
investigated the influence of the “with whom” dimension of activities to start time and
duration decisions, where ‘with whom’ refers to joint participation by family members,
household members and/or friends. Using a hazard-based modeling approach, these
two choice facets are considered as continuous variables. In an empirical application,
they found that when household members participate jointly in a social activity, the
activity episodes are usually of longer duration and tend to start earlier on the day.
These results indicate that joint participation in activities tends to have significant
influences on duration and timing decisions.
In the present paper, we similarly consider the activity scheduling process context but
adopt a different approach. In line with a common assumption of the computational
process approach, we formulate the continuous timing and activity duration model as a
set of decision rules which are derived from choice observations using a decision-tree
induction method. Thus, the model proposed here differs from hazard-based and utility
maximization methods in the sense that a process perspective is adopted and rules
instead of algebraic equations are used to represent and predict the activity timing and
duration decisions of people/household. In this study, we consider the timing and
duration decisions for non-work activities conducted jointly by the male and female
head of a household. Specifically, this study investigates the timing of non-work
activities related to household and family activities, such as household tasks (e.g.,
escorting persons, grocery shopping) and non-task activities (i.e., social and leisure
activities). Thus, the study focuses on two-heads households (with or without children)
and the joint activities in their schedules. The paper is organized as follows: the next
section gives an overview of the ALBATROSS model. This is followed by an outline
of the approach, a description of data and methods, a discussion of empirical results,
and a summary of major conclusions.
6.2 OVERVIEW OF ALBATROSS MODEL
ALBATROSS is a learning-based transportation oriented simulation systems that is
capable of simulating daily activity schedules and travel patterns of individuals and
households. It predicts for each household of a studied population the schedule of
activities and trips of each household head for a particular day. In the existing
ALBATROSS model, joint decisions involved in joint activity participations are
represented merely implicitly. In order to refine ALBATROSS such that intra-
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household interactions are better represented, in previous studies we focused on several
aspects that require joint decisions of the two household heads, such as the generation
of joint activities and the generation and allocation of household tasks (Anggraini, et, al., 2008). In addition to that, car allocation decisions, i.e. who uses the car for which
(work) activity, in case of households where the number of drivers exceed the number
of cars, was also taken into consideration (Anggraini, et, al., 2007).
In ALBATROSS, the activity types are grouped into fixed activities and flexible
activities. Mandatory activities are considered fixed activities, while non-mandatory
activities are termed flexible activities. Given the purpose of modeling household-level
decision making in an activity scheduling process, we distinguish within the category
of flexible activities household task activities and non-task activities. Mandatory
activities include work, business and other mandatory activities (e.g., school). A
household task activity refers to an activity that can be allocated to different household
members. A non-task activity is a discretionary activity that can be conducted anytime
by any person in the household. Household-tasks include the following activity types
in order of priority: (1) bring/get person, (2) shopping (one-store), (3) shopping
(multiple stores), and (4) service-related activities. Non-task activities include the
following activity types also in order of priority: (1) social visits, (2) leisure activities
(other than touring), and (3) touring (by car, bike or on foot). Since task as well as non-
task activities can be conducted jointly, we have in total 7 non-work activities to be
considered in the present analysis. In the diary data used for estimation, a joint (non-
work) activity in a household is identified as a particular non-work activity that occurs
in the diary of both the male and female head and takes place at the same location with
approximately the same start time (+/– 15 minutes) and approximately the same
duration (+/– 15 minutes).
In the ALBATROSS model, the timing of task and non-task activities takes place at
some stage in the activity scheduling process (Figure 6.1). Joint activities have priority
over independent activities and, hence, are scheduled first. Each time after having
added a joint activity of a particular type, if any, to the schedules of the two persons, a
duration and timing decision is made before adding a next activity is considered. This
means that at the moment a timing and duration decision is made for an added joint
activity only the mandatory activities (work/school/business) and joint activities of
higher-priority categories, if any, are known. It is noteworthy that each decision in this
process model is modeled by a decision tree whereby the results of earlier decisions are
used as conditions for each next decision. Decisions made are transformed in
operations on an evolving schedule. The process result is a complete schedule for each
person.
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Generating Work
Activity
• Person-Level: # episodes, Start time, Duration, Location
• Household-Level: Car allocation to work place
• Person-Level: Transport mode to work place
Generating Business
& Other Mandatory
Activity
• Person-Level: # episodes, Duration, Start time, Link-Work,
Location
Generating Task
Activities and
Non-Task Activities
• Household-Level:
- Activity selection of joint activity categories
- Activity Allocation (for allocated activities)
• Person-Level:
- Activity selection of independent non-task activities
Timing of Task
Activities and Non-
Task Activities
Trip-Chaining Choices
Location of Task
Activities and Non-
Task Activities
Transport Mode of
Non-Work Tours
• Household-Level (if Joint): Duration, Start time
• Person-Level (if Independent): Duration, Start time
STOP
START
• Household-Level (if Joint)
• Person-Level (if Independent)
Car Allocation Decisions
for Non-work Tours • Household-Level
• Household-Level (if Joint)
• Person-Level (if Independent)
FIGURE 6.1 Household Activity-Travel Scheduling Process of
ALBATROSS
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6.3 DATA DESCRIPTION
The data used for deriving the decision trees originates from the Dutch National Travel
Survey (MON = Mobiliteit Onderzoek Netherlands) collected in 2004 covering all of
the Netherlands. The survey is conducted on a regular basis to obtain travel and activity
information of residents in the Netherlands. It is a household survey where data is
collected of all household members for the diary day as well as general information
about household and individual attributes such as, gender, age, vehicle ownership and
driving license ownership, home location, individual income, occupation, number of
working hours per week, etc. Respondents were also requested to give information
about all trips made on a designated day as well as on the activities conducted on trip
destinations. Information for each trip includes start time, trip purpose, destination,
activity type at the destination, and transport mode. Situational variables are reported
as well. All in all, this survey provides a comprehensive data source to analyze
activity-travel behavior of Dutch residents. In the data collection, 29,221 households
filled out a one-day travel/activity diary and 28,600 of these households fit the criteria
for being considered in ALBATROSS. The data were transformed to an activity-diary
data format for the current estimation purpose. Given the present focus on two-heads
households, households consisting of a single head are not included in the analysis here.
This leaves 18,037 households for the present analysis. As the focus of this paper is on
modeling joint activities, a total of 4,515 days of households and 6,526 joint activities
were identified and used for deriving decision tree models.
The description of the MON data employed in this study is as follows. About 44.7% of
these households are zero-workers households, 32.3% are two-worker households, and
the rest is one-worker household.
TABLE 6.1 Independent and Joint Activity Frequency (percentage)
Weekday Saturday Sunday Activity
Indep Joint Indep Joint Indep Joint
Bring/Get 18.65 2.57 5.02 1.86 5.91 2.19
Shop-1-Store 30.34 30.07 39.02 30.18 4.04 3.29
Shop-N-Store 5.01 7.34 7.83 9.61 1.08 1.29
Service 8.23 9.35 3.93 2.44 4.63 1.48
Social 12.37 22.05 15.84 28.89 26.43 42.16
Leisure 15.78 17.03 19.48 18.78 33.02 27.31
Touring 9.62 11.59 8.87 8.24 24.90 22.27
Total 27448 3582 4677 1395 2032 1549
Note: Indep = independent activity
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TABLE 6.2 Average Duration (minutes)
Weekday Saturday Sunday
Activity Indep
St.
Dev Joint
St.
Dev Indep
St.
Dev Joint
St.
Dev Indep
St.
Dev Joint
St.
Dev
bring/get 21.26 36.45 30.47 40.6 27.3 51.8 34.19 47.22 30.11 41.64 44.26 65.63
shop-1-
store 53.8 53.25 69.29 62.57 57.43 54.98 72.90 63.26 56.27 66.97 102.39 74.22
shop-n-
store 82.06 66.91 100.32 70.59 89.44 75.38 103.47 69.56 134.09 93.83 166.75 105.47
service 63.15 67.52 79.45 73.4 55.05 60.69 62.09 44.51 46.64 70.26 55.78 56.34
social 132.32 112.5 172.12 120.24 158.67 131.13 211.78 139.26 148.25 118.89 183.93 117.90
leisure 128.21 99.53 145.93 129 151.03 134.07 149.51 126.42 142.16 111.58 133.43 110.15
touring 29.47 84.47 78.85 150.65 43.44 103.91 102.81 164.29 48.80 99.21 70.51 118.75
Ave Dur 69.03 85.01 108.36 112.52 91.36 106.77 131.83 126.28 105.93 114.95 137.00 122.49
NOTE: Ave Dur = average duration ; St. Dev = standard deviation
0
200
400
600
800
1000
1200
start time
frequency
BR
SH1
SHN
SER
SOC
LEI
TOU
FIGURE 6.2 Start-Time Profiles every 30 minutes for each Activity
Concerning car ownership, 67.44% of the 4,515 households own 1 car, 27.2% have 2+
cars, and only 5.3% have no car. In terms of household income, the high income
households are in the majority (34.2%). It is followed by low-medium income
households (29.6%) and medium-high income households (25.8%). Only 10.4% of the
household is categorized as low income household. In terms of the presence of children
younger than 18 years old in the household, around 74.6% of these households have no
young children. The rest of the households are split into 11.5%, 7.4%, and 6.6% for
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having children younger than 6 years old, 6-11 years old and 12-17 years old,
respectively. These results indicate that, although the percentage of two-worker
households is lower than zero-worker household, their average income is relatively
high.
As mentioned, the focus of this study is on joint activities. Nevertheless, the frequency
of activities that are conducted independently, which is termed as “Indep” in short, is
also revealed here, in a broader context involving 18,037 households. For the next two
tables, we will see how each activity is distributed across independent and joint
activities during a weekday, Saturday and Sunday. An independent and joint activity
frequency of each activity type is presented in Table 6.1. The predominant activity
pursued independently is one-store-shopping on Saturday, which accounts for 39.0%.
During weekday, one-store-shopping is the dominant (30.3%) independent activity. As
for joint activities on Sunday, social is the most common purpose (42.2%) and it is
followed by leisure (27.3%).
Table 6.2 shows the mean duration (in minutes) of activities. Regarding the mean
duration, joint-social is the activity that has the longest duration, on all days, which
takes 212 minutes (3 hours 32 minutes) on Saturday, 184 minutes (around 3 hours) on
Sunday, and 172 minutes (2 hours 52 minutes) on weekday. Similar to the joint
category, in the independent category, social also has the longest average duration, on
all days, which account for 159 minutes (2 hours 39 minutes) on Saturday, 148 minutes
(2 hours 28 minutes) on Sunday, and 132 minutes (2 hours 12 minutes) on weekday.
Figure 6.2 shows start time profiles of each activity (joint and independent) for every
30 minutes starting from 5 am to 9 pm. Among other activities, bring/get activities
show the highest frequency during 8.00 – 8.30 am. This activity is also done relatively
frequently jointly during midday (13.00 – 13.30 pm) and afternoon (15.00 – 15.30 pm).
Shopping to 1 store is the second activity in terms of frequency and is most often
conducted during 10.00 – 10.30 am and 11.00 – 11.30 am. Leisure activities have a
clear peak in the late afternoon and evening.
6.4 VARIABLE SPECIFICATION
Table 6.3 portrays the condition variables that were used as input to the algorithm for
induction of both decision trees. It is worth noting that some variables are related to
household level and person level, while others are defined at schedule level and activity level. Note that in this stage of the scheduling process, work, business, and other
mandatory activities are known. However, only condition information related to the
work activity is fully used for condition variables, such as number of work activities,
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duration of each work activity, mode of traveling to work place, and total time engaged
in a work activity. Continuous condition variables, such as duration, are discretisized
by using an equal-frequency interval method which divides a continuous range into n
parts, in which each part contains approximately the same number of cases.
There are two sets of household-level attributes. The first set includes household
composition, the presence of young children in a household, day of the week, socio-
economic class, and the number of cars in the household. Given the selection of two-
heads households, household composition refers to only three household types:
‘double-head, one-worker’, ‘double-head, two-workers’ and ‘double head, no-workers’.
The presence of young children in a household only considers the children younger
than 18 years old. If children over 18 years old are in a household, they are considered
as adult, not children any longer. Income is classified as follows: low (< €16,250), low-
mid (€ 16,250-€ 23,750), mid-high (> € 23,750 - € 38,750) and high (> € 38,750).
The second set of household-level variables relates to urban density and several aspects
of accessibility of locations given the home location of the household. In terms of
accessibility, 8 variables are included: (1) daily goods sector: number of employees
within 3.1 km, (2) non-daily goods sector: number of employees within 4.4 km, (3) all
sectors: number of employees within 4.4 km, (4) size of population within 3.1 km, (5)
daily goods sector: distance within which 160 employees work, (6) non-daily goods
sector: distance within which 260 employees work, (7) all sectors: distance within
which 4500 employees work, and (8) distance within which 5200 people live.
Note that attributes related to individuals can be incorporated only if they are specified
explicitly for the male and female heads. Thus, individual attributes will be tied
together with gender. They include work status and age. The work status attribute of
the male and female heads indicates whether the person has no work, part-time work,
or full-time work. Those who work more than 32 hours per week are considered as
full-time worker. Age and possession of driving license by the householders are also
defined as person-level attributes.
The following attributes are defined on the schedule level. The number of work
activities conducted by male and female is known and we limit it to maximally 2 work
episodes per person per day. The total number of work episodes across male and
female heads is included as well, which then has a maximum of 4 work episodes.
Duration of work activity conducted by male or female and the total work duration
across male and female heads in a household are also used as condition variables.
Transport mode used for the trip to the work place by male or female worker is also
used as condition variable.
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TABLE 6.3 Definitions of Condition Variables
Variable Description
Household-level
Household socio-demographics Household composition (Com) double-one-worker, double-two-worker, double-no-worker
1,2
Presence of young children (Child) no children, <6 years, 6-11 years, 12-17 years1,2
Day of the week (Day) Mon, Tue, Wed, Thu, Fri, Sat, Sun1,2
Socio-economic class (SEC) Low, Low-Mid, Mid-High, High1,2
Number of cars in household (ncar) 0, 1, 2+1,2
Land use-Accessibility measures Urban density (Urb) Population density of the residence location
1,2
Accessibility 1 (nEmp1) Daily goods sector: # employees within 3.1 km1,2
Accessibility 2 (nEmp2) Non-daily goods sector: # employees within 4.4 km1,2
Accessibility 3 (nEmp3) All sectors: # employees within 4.4 km1,2
Accessibility 4 (SizePop) Size of population within 3.1 km1,2
Accessibility 5 (Dist1) Daily goods sector: distance within 160 employees work1,2
Accessibility 6 (Dist2) Non-daily-goods sector: distance within 260 employees work1,2
Accessibility 7 (Dist3) All sectors: distance within which 4500 employees work1,2
Accessibility 8 (Dist4) Distance within which 5200 people live1,2
Individual-level Work status (wstatM/F) non-worker, part-time, full-time
1,2
Age (AgeM/F) <35 years, 35-54, 55-64, 65-74, 75+ years1,2
Driving license possession (driveM/F) 1 if male/female has driving license and 0 otherwise1,2
Schedule-level
Number of work episodes (nworkM/F/HH) maximally 2 work episodes of male, female, and household
1,2
Duration of work activity (wdurM/F/HH) total work duration of male, female, and household1,2
Mode to work place (modeM/F/HH) aggregation mode to work by male, female, and household1,2
Available time for non-work activity time available for male-female to do non-work activity1,2
(avaT1-T6)
Available time car for non-work activity car available for male-female to do non-work activity1,2
(time1C-6C)
Work is on schedule (yworkM/F) work is on schedule of male or female worker1,2
Business is on schedule (ybusM/F) business is on schedule of male or female worker1,2
Other mandatory is on schedule (yothM/F) other mandatory (school) is on schedule of male or female1,2
Activity-level
# each activity j (j= 1…7) # each joint activity j currently done in household1,2
(nbr,nsh1,nshn,nser,nsoc,nlei,ntou)
Duration of activity concerned j (j= 1…7) duration of each activity j currently done in household1,2
(dur-br/sh1/shn/ser/soc/lei/tou)
Total duration across the 7 activity types total duration across the 7 activity types currently done in
(durtot) household1,2
Activity type (acty) activity type considered (7 activities) 1,2
Duration of activity (dur) duration of the activity considered2
Note: 1 Alternative classification used for duration model (model 1)
2 Alternative classification used for start time model (model 2)
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In case multiple work episodes are included in a schedule, we aggregated the mode by
arranging modes in a hierarchy as follows (in order of priority): (1) Public Transport
(2) Car Passenger (3) Car Driver and (4) Slow (Bike and Walk). Additionally, the
available time in the current schedule to do non-work activities by both male and
female is represented. In order to identify how much time is available for these
activities at different times of the day we segmented the period from 8 am to 8 pm into
6 time spans of 2 hours. For each 2 hour-period we calculated the available time in the
schedule and classified this time into 5 categories, where zero means no time left for
doing a non-work activity and the remaining categories identify how much time is left
for doing a non-work activity in multitudes of half an hour. These variables are
computed for male and female together and, thus, indicate the time available to both.
In addition to available time, the time a car is available for a non-work activity is also
considered as a condition variable. Hereby, the number of cars available and the
transport mode(s) used for work activities, if any, of the two household heads are taken
into account. Time is segmented in the same way as before. Note that zero means that
either car is not available due to a work activity or because no car is available in the
household. The next variables indicate whether or not work, business and other
mandatory activities (such as go to school) are conducted on the given day by male or
female.
The subsequent variables are activity-level variables. The first variable at this level
indicates for each joint task and joint non-task activity category the number of
activities that, at the moment of the duration and start-time decisions, are included in
the schedule, as a consequence of previous activity selection decisions for joint
activities. Thus, these variables are dynamic and take into account the assumed priority
order of activities. At the time of decisions for the first joint activity, no other joint
activities are included in the current schedule. Therefore, all these variables are zero in
the beginning. For decisions of a second joint activity, the result of decisions related to
a first optional joint activity (i.e., a bring/get activity) is known and if this implied the
insertion of a (bring-get) activity the corresponding variable has a value of one, and so
on. In sum, this set of variables indicates for each current decision the current schedule
state as a result of previous activity selection decisions. The next series of variables at
this level indicates the total duration of each (joint) activity that, at the moment of the
decision, is included in the schedule as a consequence of previous activity selection
decisions. The way of doing it is the same as for the previous attribute. The following
variable is the total duration of activities across the 7 activity category that is currently
included in the schedule (of both heads) at the moment the decision is made. The for-
last variable encodes the activity type that is considered in the current duration/start
time decision. This variable has seven levels corresponding to the seven activity
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categories (joint task and joint non-task activities) considered in the model. The last
variable is the duration of the activity for which a decision is made. Note that, in the
sequential process, a duration decision is made before a start time decision. Hence, at
the moment a start time decision is made, the duration is known. The result of the
(previous) duration decision (as observed) is added as conditional information for the
(next) start time decision for the same activity. This means that the duration variable at
activity-level is only included as a condition in the start time model. All other variables
are used for both models.
As a result, a total of 63 condition variables were defined for the duration model as
indicated by superscript 1 in Table 6.1. For the start time model, the condition variables
are actually almost the same. The difference merely concerns the additional duration
variable at the activity-level as indicated by superscript 2 in Table 6.1. Hence, there are
64 condition variables for the start time model.
6.5 METHODS
6.5.1 Decision Tree Induction
ALBATROSS is a rule-based multi-agent system of activity-travel behavior. It consists
of a large number of rules (IF…. THEN…) for each choice facet, indicating the
choices made by individuals dependent on conditions in terms of socio-demographic
characteristics and other context variables. These rules are extracted from activity-
travel diary data using a tree induction method. Thus, these induction methods identify
the rules that describe which choices are made under which conditions. The basic
algorithm used in ALBATROSS is a CHAID-based tree induction method which
generates non-binary trees. The basic algorithm is appropriate for a categorical or
nominal action variable (also called response or decision variable), as implied by the
chi-square test statistic that is used as split criterion. However, because in this case, we
are not dealing with a categorical or nominal action variable but rather with continuous
action variables (duration and start time), the chi-square test statistic cannot be used.
Therefore, we use the F-statistic for timing and duration decision trees instead. The tree
induction algorithm, however, apart from that, remains the same. It generates a
decision tree by splitting the condition space on one condition variable at a time into
two or more subsets repeatedly, beginning with the entire data set. The split that
maximizes a significance value of the F-test across condition variables is used for
splitting if the split is significant. The process is repeated for each newly created group
until no more significant splits are found. In order to develop the duration and timing
decision tree, 75% of the cases were used for training and the remaining cases were
used for validation. Generally, in deriving ALBATROSS decision models, the
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observed choice (in this case, duration or start time) and condition variables (in this
case the ones listed in Table 6.3) are extracted from the diary data for each observation
(in this case, a joint activity).
6.5.2 Deriving Impact Tables
Decision trees derived from data may become very large and complex and,
consequently, difficult to interpret. This holds true particularly for the present
application where the number of choice observations is very large. Arentze and
Timmermans (2003) developed a method to derive elasticity information from rule-
based models to facilitate interpretation, which we will use here to describe the results
of tree induction. The method was developed for discrete decision trees, but can be
used with minor adjustment also for a continuous decision tree. The principle of the
proposed method is straightforward. After having derived a rule-based model from
training data, the model is used to predict for each condition variable the impacts on
the action variable. The model is applied to the training set as many times as there are
levels of the condition variable considered. In each run, each training case is assumed
to take on the level of the condition variable considered in that run. The mean and
standard deviation of predictions (start time or duration) across training cases under
that setting are recorded. Repeating this process yields a table with for each level of the
condition variable a distribution of the action variable defined by a mean and standard
deviation. The impact of the condition variable is then measured as the F-statistic for
this table.
6.6 RESULTS
In total 6,526 observations of joint activities can be derived from the data set. Across
these observations, the mean duration is 121 minutes (with a standard deviation of 120
minutes) and the mean start time is 2.08 pm (with standard deviation of 207 minutes).
As said, 75% of the cases were used for training and the remaining cases were used for
validation. Given a minimum group size of 75 cases at leaf nodes and a 5% alpha level,
the trees generated by CHAID consists of 17 and 31 leaf nodes (decision rules) for
duration and start time models, respectively.
The duration model consists of 17 decision rules with an F-statistic value of 74.57. The
S value, which stands for the average number of minutes mispredicted by the model
(i.e., standard error), shows a slight improvement from 120.2 minutes (null model) to
107.9 minutes (decision tree model). The start-time model consists of 31 decision rules
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with an F-statistic value of 76.55. The S value shows a more substantial improvement
compared to the duration model, namely from 206.9 minutes (null model) to 170.3
minutes (decision tree model). The results of the S value on the validation set indicate
that virtually no overfitting occurred, i.e. S = 104.0 minutes and S = 178.8 minutes for
duration and start time models respectively. In case of the duration model the average
misprediction is even slightly lower.
As explained in Section 6.5.2, the impact table analysis shows which condition
variables have an influence on the decision choice of the individual/household and the
size of the influence. As it appears, of the 63 condition variables, 7 variables have an
influence on the activity duration of joint activities. Activity type is by far the most
important variable for the duration decision, as it is the first splitter and has the highest
F-statistic (F = 1439.8). It is followed by the total duration of joint activities included
in the current schedule of the household (F = 88.8). Specifically the time available
during the afternoon off-peak period (12 – 2 pm) has an impact as well (F = 45.3).
Several individual and household attributes also have an influence. These include age
(male: F = 7.4 and female: F = 5.16) and car possession in household (F = 5.9). Finally,
spatial variables and, specifically, urban density also impacts the duration choice (F =
1.52).
For the start-time model, of the 64 condition variables, 16 variables have an influence
on the start-time choice for joint activities. As in the duration model, activity type also
has a big influence in the start-time model (F = 455). Nevertheless, the available time
to do a non-work activity during morning off-peak period is by far the most influential
variable in this model as indicated by the high F-statistic value (F = 1576.4).
Subsequently, the dynamic variable indicating that a particular joint activity is included
in the current schedule of the household when the decision is made has an influence in
the start-time model (joint-leisure: F = 75.9 and joint-touring: F = 1.1). Duration of
social activities currently done in the household (F = 59.6) also influence the start time
decisions. Day of the week is a next variable that influences the start-time decision for
doing joint activities (F = 37.4). Accordingly, the total duration of joint activities
currently included in the schedule of the household (F = 29.3) and the duration of the
activity currently considered (F = 22.1) have an influence in the model, which suggests
that there is an influence of duration decisions on start time decisions. On the other
hand, individual and household attributes influence the start time decisions as well,
such as household income (F = 4.2), household type (F = 2.6), age of male (F = 1.3),
presence of young children (F = 1.05), and car ownership of the household (F = 0.75)
and accessibility measures (accessibility 7: F = 0.8 and accessibility 1: F = 1.8).
Availability time in the afternoon to do a non-work activity shows an impact as well (F
= 2.4).
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TABLE 6.4 Duration Tree Model
Rule # Urb AgeM AgeF ncar Ava T3 durtot acty Average Duration St. Dev
1 - - - - - - 1 35,25 50,63
2 - - - - 0-1 0 2,4,7 47,26 43,87
3 - 0-1 - - 2-4 0 2,4,7 82,95 89,34
4 - 2 0-1 - 2-4 0 2,4,7 144,45 216,77
5 - 2 2-4 - 2-4 0 2,4,7 85,12 101,59
6 - 3-4 - - 2-4 0 2,4,7 74,52 92,20
7 - - 0 - - 1-4 2,4,7 88,13 107,09
8 - - 1-4 - - 1-4 2,4,7 56,75 81,44
9 - - - - - - 3 105,23 75,28
10 - - - - 0-3 0-1 5 157,08 80,99
11 - - - - 4 0-1 5 200,49 129,66
12 - - - 0-1 - 2-4 5 144,15 109,77
13 - - - 2 - 2-4 5 174,74 114,72
14 0-2 - - 0-1 - 0-1 6 171,60 155,98
15 3-4 - - 0-1 - 0-1 6 133,99 113,04
16 - - - 2 - 0-1 6 179,47 158,88
17 - - - - - 2-4 6 120,42 86,86
To interpret these results, the underlying decision trees are shown in Table 6.4 and
Table 6.5 below. To explain the table format, we describe one arbitrary decision rule as
an example for each model. For example, Rule #9 of the decision tree for duration says
“IF the activity considered is shopping to multiple stores, THEN the average duration
is 105 minutes (standard deviation is 75 minutes)” and Rule #4 of the start time
decision tree “IF there is no time available in the morning off-peak period AND the
available time at 2 – 4 pm is 1.5 – 2 hours AND the activity considered is a (joint)
social, leisure or touring activity, THEN the average start time is 17:46 hour (1067
minutes) (standard deviation is 152 minutes)”.
Several relationships are revealed. First, for duration choice, the tree reveals that the
influence of activity type is consistent with the patterns that we saw in the descriptive
analysis and, hence, is as expected. When the schedule already includes a joint activity
the duration of a next activity becomes shorter. Furthermore, the duration of a joint
social activity increases with time available for non-work activities. These patterns
indicate that particularly time-budget and travel–time considerations influence duration
decisions in ways as could be expected. Presence of 2 or more cars in the household
tends to increase the duration particularly for social and leisure activities. Leisure
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activity takes longer duration in high urban density areas than in low urban density
areas.
As for the start-time model, the underlying decision tree (Table 6.5) indicates that joint
activities occur only very rarely in schedules where one of the household heads have a
work activity between 10 am and noon and when this occurs the start time is
substantially earlier. The effects of activity type are as expected and in line with the
patterns that emerged from the descriptive analysis. The presence of a previously
scheduled joint activity leads to a later start time for a next joint activity. Furthermore,
Sunday leads to earlier start times, if no joint activity has been scheduled yet. The
presences of young children in the household influences household heads decision to
start earlier performing a joint bring/get activity. Presence of two or more cars in a
household tends to leads to a later start time. Higher income households tend to start
later performing a joint social activity, especially, on Monday and Sunday when longer
time is available in the morning. In non-worker households, having already scheduled a
joint activity leads to a choice of an earlier start time for a next joint activity. In sum,
these patterns suggest that scheduling considerations play a dominant role in start time
decisions, whereas other factors have relatively small impacts.
6.7 CONCLUSIONS AND DISCUSSION
This paper presented the development and testing of models for duration and timing
choice for joint household-task and non-task activities as part of a full-fledged activity-
travel scheduling model for a refined ALBATROSS. Focusing on joint participation
between the two household heads in non-work activities, a rule-based duration and start
time model using a CHAID-based algorithm was derived from activity-trip diary data.
Decision tree results indicated that there were 17 and 31 condition-action rules derived
for the duration model and start time model, respectively. The improvement in S-value
(a measure of prediction accuracy) relative to a null model as well as an F-statistic
indicates that there is a moderately strong association between condition variables at
household, individual, activity and schedule level, on the one hand, and the decision,
on the other. The S-value shows a more substantial improvement in the start-time
model compared to the duration model. The results of the S-value on the validation set
indicate that virtually no overfitting occurred, i.e., that the rules are generalizable to
unseen cases. As an impact analysis of the decision-tree models showed, activity type
has a substantial influence in both the duration and start-time model. Within activity
categories, duration decisions seem to be primarily driven by time-budget and travel-
time considerations.
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TABLE 6.5 Start-Time Tree Model
Rule
# Com Child Day SEC AgeM nEmp1 Dist3 ncar
Ava
T2
Ava
T4 nlei dursoc durtot acty dur
Ave
Start-
Time St. Dev
1 - 0 - - - - - - 0 - - - - 1,4,2,3 - 1005,33 164,27
2 - 1,2,3 - - - - - - 0 - - - - 1,4,2,3 - 890,90 247,09
3 - - - - - - - 0 0-2 - - - 5,6,7 - 1172,34 96,88
4 - - - - - - - 0 3-4 - - - 5,6,7 - 1066,82 152,09
5 2,3 0,2,6 - - - - - 1-4 - 0 - - 1,6 0-2 824,88 229,28
6 4 0,2,6 - - - - - 1-4 - 0 - - 1,6 0-2 737,25 204,25
7 - 0,2,6 - - - - - 1-4 - 0 - - 1,6 3 708,90 190,75
8 - 0,2,6 - - - - - 1-4 - 0 - - 1,6 4 782,70 223,36
9 - 1,3,4,5 - - - - - 1-4 - 0 - - 1,6 - 854,06 219,77
10 - - - - - - - 1-4 - 1-2 - - 1,6 - 989,69 176,59
11 - - - 0-2 - - - 1-4 - - - 0 2,3,4 0 797,87 169,68
12 - - - 3-4 - - - 1-4 - - - 0 2,3,4 0 733,39 161,70
13 - - 0-1 - - - - 1-4 - - - 0 2,3,4 1-4 713,71 142,21
14 - - 2-3 - - 0-2 - 1-4 - - - 0 2,3,4 1-4 757,13 142,76
15 - - 2-3 - - 3-5 0-1 1-4 - - - 0 2,3,4 1-4 710,38 133,48
16 - - 2-3 - - 3-5 2 1-4 - - - 0 2,3,4 1-4 746,14 135,52
17 2,4 - - - - - - 1-4 - - - 1-4 2,3,4 - 783,54 137,51
18 3 - - - - - - 1-4 - - - 1-4 2,3,4 - 841,17 126,03
19 0,6 0-1 - - - - 1-4 - - 0-1 - 5 - 813,79 164,09
20 0,6 2-3 - 0-1 - - 1-4 - - 0-1 - 5 - 884,32 157,52
21 0,6 2-3 - 2-5 - - 1-4 - - 0-1 - 5 - 827,05 171,48
22 1,3,2 - - - - 1-4 - - 0-1 - 5 - 875,26 211,28
23 4,5 - - - - 1-4 - - 0-1 - 5 0-3 892,54 195,80
24 4,5 0-1 - - - 1-4 - - 0-1 - 5 4 1035,17 186,10
25 4,5 2-4 - - - 1-4 - - 0-1 - 5 4 949,53 227,60
26 - - - - 1-4 - - 2-4 - 5 - 1016,75 155,42
27 - - - - 1-4 - - - 0 7 0-1 856,75 151,69
28 - - - - 1-4 - - - 0 7 2-4 750,47 125,54
29 - - - - 1-4 - - - 1-4 7 0-1 900,86 176,61
30 - - - - 1-4 - - - 1-4 7 0-1 964,04 166,45
31 - - - - 1-4 - - - 1-4 7 2-4 840,67 131,46
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There is, however, substantial heterogeneity across cases, which is only partly related
to day of the week and socio-demographic variables. More than in case of duration
choice, start-time decisions are influenced by scheduling opportunities. In particular,
the available time during the late morning in the schedules of the two heads, given their
work activities (if any) appears to be crucial. Some individual and household
characteristics such as household type, presence of children, income, car availability,
and age of male show a big influence on start time decisions. In both models, car
availability and age of male play also a modest role.
Finally, the results indicate that there is a relatively strong influence of duration choice
on start-time decisions. This means that a state-dependent sequential decision process,
as used in ALBATROSS, works well. Consistently with Habib et. al (2008), we find
that joint participation of household members in activities tends to lead to a longer
activity duration. Furthermore, joint activities by household heads tend to start earlier
in the day especially when this involves a bring/get activity (e.g., bringing a child to
school). These results suggest that the decision tree induction method can also capture
the joint decision making in household.
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Chapter 7
HOUSEHOLD LOCATION CHOICE MODELS FOR
INDEPENDENT AND JOINT NON-WORK
ACTIVITIES
Anggraini, R., Arentze, T.A., and Timmermans, H.J.P., 2009. Paper presented at XIII Euro Working Group on Transportation, Padua, Italy.
ABSTRACT Modeling location (destination) choice has a long history in transportation research and in disciplines such as urban planning, geography and regional science. The vast majority of this literature is concerned with the problem of how individuals choose a destination. However, trips may involve household and hence it could be argued that conceptualizing destination choice as a problem of individual choice behavior may be inadequate, especially in case of joint household activities. Therefore, we develop the household location choice model taking into account the heads of household (male-female) independent and joint activity, in particular in non-work activities. The model is incorporated in the activity-scheduling model of ALBATROSS – an activity-based model of travel demand, predicting travel demands in an activity-based micro-simulation system. In this paper, we examine the location choice model for independent and joint activity participation of the household heads based on the concept of detour time. To deal with a large set of attribute variables and account for non-linear relationships between the variables, a CHAID decision tree induction method is used to derive a decision tree model. There are two models incorporated in this study: (i) determining whether the activity is conducted on the same location as the previous activity, the same location as the next activity, or at some other location and, if at some other location, (ii) determining the location in terms of a combination of size class and distance class of the postcode area. The performance of the location choice model for joint activity turns out to be superior to that of the independent activity, in particular for the first model. The tendency of male and female performing multiple activities at the same location is higher when travelling alone than travelling together.
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7.1 INTRODUCTION
Activity-based modeling of travel demand has gradually shifted from academic
exploratory models to full-fledged operational models which are now being applied in
transportation planning practice. Activity-based approaches have augmented our
conceptualization of travel behavior by including mutual dependencies, for example
between the activity-travel schedules of different household members. Although the
necessity to include multiple households members have been identified from the very
beginning of activity-based analysis, the topic of household decisions has still received
relatively scant attention.
According to Gliebe and Koppelman (2002), employment commitments and childcare
responsibilities have significant effects on trade-offs between joint and independent
activities. Chandraskharan and Goulias (1999) found that joint activities are
appreciably affected by household size, and age of the household members. In addition,
car ownership levels have been observed to be positively correlated with individuals
following independent paths over the course of a day. Likewise, Golob and McNally
(1997) also modeled the interactions between household heads. Although not
considering joint activity participation, they found the relationships between time
allocated to work, maintenance, and discretionary activity, and to the travel generated
by each activity.
It has been recognized that joint activity participation modeling is one of the more
complicated behavioral patterns in activity-based travel modeling. In particular in
multi-persons households, definitions of joint activity vary. Gliebe and Koppelman
(2002) identify activities as being joint-in-purpose, joint-in-location, joint-in-time, or
some fuzzy subset along those dimensions. In this study, we focus on household
decision making with regard to activity location choice, in particular for the two heads
of household. We, further, intend to analyze the location decisions for independent and
joint activities.
An understanding of the factors that influence the choice of location can contribute to
more effective land-use and zoning policies. To better understand activity choice
location in the context of a complete activity schedule for a day, the concept of detour time is applied. This concept is used in ALBATROSS (Arentze and Timmermans,
2007). Different from any other concept that considers the travel distance from home
or non-home to a particular location, detour time considers relative location to the
previous and next activity. The detour time of a candidate location for an activity is
defined as the extra travel time required to implement the activity in the context of the
current activity schedule. This concept is very useful to build trip chains and to
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simulate the emergence of feasible activity-travel schedules that take space-time
constraints into account.
This paper reports the results of modeling activity location decisions for independent
and joint activities from the perspective of household decision making. The results of
this paper should be viewed as a step in refining ALBATROSS (Arentze and
Timmermans, 2000, 2004, and 2005), but the results are also relevant in their own right.
The paper is organized as follows: the subsequent two sections present a synopsis of
the location decision model in current models. This is followed by an outline of the
approach, adopted in this study, a description of the data, a discussion of empirical
results, and a summary of major conclusions.
7.2 LOCATION DECISIONS IN THE EXISTING MODEL
Arentze and Timmermans (2007) developed a location choice model in which location
choice decisions are made in a priority order of activities and within activities in the
order in which the activities occur in the schedule. The model uses the concept of
detour time. The detour time of a candidate location for an activity is defined as the
extra travel time required to implement the activity in the context of the current activity
schedule. Let xi be the origin of the trip to a candidate location i and yi be the
destination location of the trip from i. Thus, the detour time related to location i is
defined as follows:
dti = dt(xi, i) + dt(i, yi) – dt(xi, yi) [7.1]
where, dt(i, j) is the travel time between locations i and j.
In order to estimate the detour time for a particular location, the origin and destination
location should be known. The extent to which this information is available at the
moment of the decision depends on the activity type. ALBATROSS considers several
activity types in general: work, work-related such as business and school, and non-
work activities such as escorting, shopping (daily and non-daily), service-related, social,
leisure, and touring. Location decisions for those activities are made at different
moments in the sequential scheduling process.
As a consequence of the sequential decision process, the available information for
location decisions is limited. At the moment the location decisions are made, the
following information is available. Generating a work activity is the top priority in the
ALBATROSS scheduling process. It includes information about the number of work
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episodes, start time and duration, and the location and mode choice of the work tour.
Furthermore, it takes work-related activities (business and other mandatory) into
account. The subsequent process generates non-work activities, such as escorting,
shopping, service, social, leisure and touring. The first three activities are considered to
be a household task, while the last three activities are non-household activities (even
though they may be conducted jointly). Household tasks need commitment from
household heads, such as who delivers the children to school. As a result, a task
allocation decision is needed to be made. Joint participation in activities also needs
trade-offs between adult heads of household. This can be applied to either household
task or non-household activities. Having scheduled all activities in a priority order,
duration, timing and trip-chaining decisions are established. Subsequently, location
decisions are simulated.
In case of work and work-related activities, the non-work activities are not yet
scheduled and the location of the next (work or work-related) activity, if it is other than
home, is still unknown. In such cases the model assumes that the next location is the
home location. On the other hand, in case of a non-work activity, the location of the
next activity is unknown if it is a non-work activity of a same or lower priority for the
same reason. Again, in such cases the model assumes that the next location is the home,
work location or the location of a higher priority activity (what comes first in the
schedule). Although these assumptions are simplifications of reality, it is to be
expected that they will not seriously affect the performance of the model. At least, the
model is able to take into account the location relative to home and to a previous/next
location in every location decision of a sequential priority based scheduling process
and consequently it should better cover interdependencies in these choices. A space-
time prism is calculated for each location decision defining the set of locations that are
within reach given the space-time constraints imposed by the interaction between the
environment and the schedule.
Having identified the origin and destination for the activity considered, the model
determines the locations, based on postcode areas, which are within reach, i.e. within
the prism. Since transport mode is unknown yet, the model calculates a preliminary
prism based on the fastest transport mode available in the time slot under concern. For
instance, if the person is a driver and the household owns a car(s), and the car is not
used for a work activity of another household member in the same time slot, then the
fastest travel mode is the car. In case there is no car in the household, the fastest travel
mode is public transport in most cases. Having identified the fastest transport mode,
the shortest travel time across the road network is determined. Furthermore, the
(minimum) duration of the activity, the time window and opening hours of required
facilities at destination, are taken into consideration in the model.
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Time window is defined by the earliest possible departure time from the origin and the
latest possible arrival time at the destination. All in all, the resulting set of locations
meet an exhaustive set of space-time and resource availability constraints.
This conceptualization is similar to the current version of ALBATROSS. However, the
existing model only applies to the context of person-level decision making
(independent activities) while in this study we expand it to cover as well household-
level decision making (joint activities).
Figure 7.1 shows the procedure assumed in the current model. The model basically has
two sub-models, both represented by decision rules modeled as a Decision Tree (DT).
The first tree (DT1) determines whether the activity is conducted at the same location
as the previous activity (in the schedule), the same location of the next activity or at
some other location. In the second tree (DT2), a choice from the set of locations
available in the prism is made. It determines the location in terms of a combination of
size class and distance class of the postcode area. The size class depends on the
activity type under concern and the size of available facilities at the activity location
(i.e., number of employees in the relevant sector). Size is classified into 5 categories
START
STOP
Same as PREVIOUS Size by distance
band of activity i
i = 1 i > I i = i + 1
Select location
from band
i = index of activity episode in order of occurrence in the schedule (i = 1….I)
DT1
DT2
Relative location of
activity i
Same as NEXT
OTHER
FIGURE 7.1 The Process Model for Predicting Location of Non-
Work Activities
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based on employment in the relevant sector for the activity considered (e.g., the retail
sector in case of a shopping activity) and distance is classified in terms of a detour
travel time (by car) also into 5 categories. Hence, the choice alternatives for the DT
consist of 25 location classes. Given the prism and the choice of a location class, the
exact postcode area is determined by Monte Carlo simulation, if there are multiple
locations of that class within the prism. Furthermore, a distance decay factor is taken
into account so that locations with longer detour time within the class have smaller
probabilities of being selected.
7.3 HOUSEHOLD LOCATION DECISIONS (JOINT ACTIVITY)
The above conceptualization is implemented in the current version of ALBATROSS.
However, the existing model only applies to the context of person-level decision
making (independent activities) while in this study we expand it to cover as well
household-level decision making (joint activities). This will be the subject of this
section.
Since we consider the activity that is performed independently or jointly by male-
female heads, each DT model is applied to those. Independent activities are treated
practically the same as in the current version of ALBATROSS, where one of the heads
conducts a particular non-work activity singly. In contrast, the joint activity model is
applied when both adult heads of household perform the same activity at the same start
time (+/- 15 minutes), the same duration (+/- 15 minutes) and the same location (based
on postcode area). The space-time prisms are determined for the two persons jointly.
That is to say, a location is within reach only if it is within reach for both persons given
their schedule context settings.
As in the current model, each independent and joint activity model consists of two sub
models. The first model consists of 3 choice alternatives: the activity is conducted at
the same location as the previous location, the activity is conducted at the same
location as the next location or other. In case of the latter, it proceeds to the second
model that consists of 25 choices as explained above. As a result, four DTs are
generated. The DTs concerned with the joint location decisions are defined on
household level and, in that sense, represent rules of joint decisions between the
household heads.
Fundamentally, the attributes (i.e. condition variables) used in independent and joint
activity models are equivalent, i.e. relate to the same dimensions. There are four
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categories, person-level, household-level, schedule-level, and activity-level attributes.
For more details about the condition variables, see Section 7.5.
Although the dimensions of the variables are the same, some operational decisions
need to be made for joint activities when the schedule contexts differ. In specific,
variables related to a person, schedule or the activity need to be merged to represent the
relevant conditions for the two persons together. Since a location should meet
requirements of both persons, the most restrictive requirements are taken as an
indicator. This means that a minimum (in case of resources) or maximum (in case of
demands) is used for each attribute variable across the two persons to arrive at the
corresponding attribute variable at joint-person level.
To some extent, there are additional remarks for joint activity variables, in particular,
the activity-level attributes. First of all, the start times and durations of the joint activity
considered are not necessarily exactly the same. If the time is different, the model takes
the minimum start time and duration across the persons. The previous or next activity
in the schedule may also be different between the persons. Following the above rule,
the model takes the minimum or maximum of schedule context variables. For instance,
if the distance (measured as travel time by car) of the previous activity location from
home is different the maximum is taken. As another example, if available facilities
(measured as number of employees in the relevant economic sector) at the previous
location or next location differ we take the minimum value. This is done to identify the
worst case that might be occurred.
Regarding other schedule-level attributes, the attributes consist of the number of
episodes of each particular activity and the total duration (for 10 activity categories,
ranging from work to touring). Assuming that the highest activity load is indicative for
available time, we take the maximum of these indicators. For example, if the number of
daily shopping episodes on the day of the male is 1 and that of the female is 3, then, the
number of daily shopping episodes used to describe the conditions for the choice is 3
for that particular household. A similar treatment also applies for the total duration of
each activity.
Household-level attributes of the joint activity model, obviously, are the same as that of
the independent activity model. For person-level attributes the information of male and
female are merged (age, driver license possession, work status) or become redundant
and undefined (gender). In case of joint activities, the definition of a space time prisms
takes into account the schedule settings of both persons involved. A location is within
reach only if it is in reach for both persons.
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7.4 DATA
The data source used for empirical analysis of the new as well as the old model is
based on the 2004 Dutch National Travel Survey (MON data) which covers all of the
Netherlands. The survey is conducted on a regular basis to obtain travel and activity
information of residents in the Netherlands. The survey is a traditional trip-diary survey
and not an activity or time use survey but nevertheless includes relatively detailed data
on activities at trip destinations. The survey is conducted as a mail-out mail-back
survey. It is a household survey where diary data is collected of all household members
on a designated day. As a result, general information about household and individual
attributes such as, gender, age, vehicle ownership and driving license ownership, home
location, individual income, occupation, number of working hours per week, etc, were
collected. Diary days of individuals that do not include any trip are included as well.
Information for each trip includes start time, trip purpose, destination, activity type at
the destination, and transport mode. Situational variables and spatial geography are
also reported. All in all, this survey provides an exclusive data source to analyze
activity-travel behavior of Dutch residents.
The 2004 survey instrument was mailed to about 40,000 households and 29,221
households filled out a one-day travel/activity diary. Of these, 28,600 households fit
the criteria for being considered in ALBATROSS. The data was transformed to an
activity-diary data format for estimation purposes. The present study focus on the
location decision of household heads either in independent or joint activity. The
number of households performing an independent activity is 19,500, while there are
49,793 incidents of independent activities. A total of 5,017 two-head households
conduct a joint activity. The database contains 7,150 joint activity episodes. The data
were combined with extensive national datasets about the transport network and land-
use system. The study area is all of the Netherlands. Postcode areas are taken as the
unit of location (at a 4-digit level of which there are about 4000 in The Netherlands).
Land-use data include employment by economic sector for each postcode area. Data
about the transport system includes the road network for car and bike and zonal travel
times and travel costs for public transport modes. Finally, the database includes
opening hours of stores and parking facilities at locations.
7.5 OVERVIEW OF CONDITION AND ACTION VARIABLES
The definition of condition variables essentially depends on a particular decision under
consideration. The choice of condition variables is limited by the database that can be
used in the prediction phase. Furthermore, the choice is lead by theoretical
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contemplations of which variables are prospectively relevant for a given decision. As
said, for this study, condition variables are related to household-level (H), person-level
(P), schedule-level (S) and activity-level (A). The choice of condition variables at
household-level and person-level are mostly used in either of the two sub-models.
At schedule level, condition variables illustrate the history of the decision process
where the model begins with an empty schedule, and then proceeds to the next
decisions that become available in the individual/household activity schedules. On the
level of the activity, the condition variables are related to specific information about
the activity concerned. In sum, condition variables are defined as input to the model
algorithm to find the rules that suit the output decision (action variables). The condition
variables that we used as input to the DT induction are portrayed in Table 7.1.
Table 7.1 shows the condition variables that we use for independent and joint activities.
The number of condition variables is nearly the same for both models, about 41
variables. However, since the activity is done together, gender is not relevant for joint
activities. Another variable that measures the maximum travel distance partner across
fixed activities in same time window is also not relevant. Hence these variables are left
out, remaining 39 variables for joint activities.
Urban density (of the residence location) is a household-level attribute that consists of
5 classes with ascending order from most densely to least densely areas. Household
composition is also a household-level attribute that consists of 5 categories: single-non-
worker, single-worker, double-single-worker, double-dual-workers, and double-non-
workers households. Nevertheless, in case of joint activities, only the three last
categories are relevant.
Age of the youngest child in a household is a household-level variable as well. It has 5
categories: no children, children less than 6 years old, children 6 – 11 years old, and
children 12 – 17 years old. Young people ≥ 18 years old are no longer considered as
children. Other household-level attributes are household income and car ownership.
Household income has 4 classes: low income (≤ € 16, 250), low-mid income (€ 16,251
– 23,750), mid-high income (€ 23,751 – 38,750) and high income (> € 38,750)
households. Car ownership accounts for the number of cars in the household and has 3
categories: no cars, 1 car, and 2 or more cars. Finally, day of the week consists of 7
days that begins from Monday to Sunday.
The following attributes are defined at the personal-level. Age of people has 5 classes:
younger than 35 years old, 35 – 54 years old, 55 – 64 years old, 65 – 74 years old and
75 years old or older.
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TABLE 7.1 Condition Variables of Independent and Joint Activity
No Label Definition In Any Cases
1 Urb Urban size As is
2 Comp Household composition As is
3 Child Presence of young children As is
4 Day Day of the week As is
5 pAge Age As is
6 SEC Household income As is
7 Ncar Car ownership As is
8 Gend Gender Undefined – not used for joint act
9 Driver Driving license holder Is maximum (Joint is driver if at least
one person is driver)
10 Wstat Work status of Male Work status of male
11 PWstat Work status of Female Work status of female
12 Wodur Work duration Is maximum
13 Aty Activity type As is (except business and other)
14 Adur Activity duration As is (if different, take minimum)
15 Abt Activity start time As is (if different, take minimum)
16 VoisA Current activity = previous activity Is maximum priority (0=both home,
else minimum)
17 Voty Activity type of previous activity Is maximum priority (0=both home,
else minimum)
18 VoH Previous location is home Is product (1=both home, 0=otherwise)
19 Vosize Size of previous location Is minimum (worst case)
20 Vopark Parking price at previous location Is maximum (worst case)
21 Vohtt Travel time by car previous location - home Is maximum (worst case)
22 Vogord Order of municipality of previous location Is minimum (worst case)
23 NaisA Current activity = next activity Is maximum priority (0=both home,
else minimum)
24 Naty Activity type of next activity Is maximum priority (0= both home,
else minimum)
25 NaH Next location is home Is product (1=both home, 0=otherwise)
26 Nasize Size of next location Is minimum (worst case)
27 Napark Parking price at next location Is maximum (worst case)
28 Nahtt Travel time by car next location - home Is maximum (worst case)
29 Nagord Order of municipality of next location Is minimum (worst case)
30 Tavail Available time Is minimum (worst case)
31 LvoisLNa Previous location = next location Is product (1=both home, 0=otherwise)
32 Ptt Max travel distance partner across fixed activities in same
time window Undefined – not used for joint act
33 CarAv Car availability Is maximum (best case)
34 MxSizeD1 Max size of relevant sector in distance band 1 Arbitrary: take Male’s
35 MxSizeD2 Max size of relevant sector in distance band 2 Arbitrary: take Male’s 36 MxSizeD3 Max size of relevant sector in distance band 3 Arbitrary: take Male’s 37 MxSizeD4 Max size of relevant sector in distance band 4 Arbitrary: take Male’s 38 Dmin The nearest band where a location is available in prism Same for both
39 Dmax The farthest band where a location is available in prism Same for both
40 Smin The smallest location size category available in prism Same for both
41 Smax The largest location size category available in prism Same for both
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Other personal-level attributes are driving license possession, and work status of male
and female. Driving license possession is a binary variable: yes or no. In case of joint
activities, the presence of at least one person with a driving license is indicative. The
work status attribute of the male-female heads indicates whether the person has no
work, part-time work, or full-time work. Those who work more than 32 hours per week
are considered full-time workers. In addition to these attributes, only one schedule-
level variable is identified, i.e. totals work duration of the male or female. In the
context of joint activity, the maximum work duration of either the male or female is
taken into consideration.
The subsequent attributes are defined at the activity-level (#13 - #41). There are 9 types
of activities being considered (except work activities) as possible independent activities.
These include business, bring/get, shop-1-store, shop-n-store, service, social, leisure,
touring, and other. As for joint activities, the model excludes business and other
mandatory activity meaning that 7 activities remain as possible activities for joint
participation. Activity duration and start time are continuous variables. However, they
are discretisized to four levels. Variables #16 - #22 cover information about the
previous activity setting, while variables #23 - #29 contain information about the
setting of the next activity. For instance, variable #16 states whether the current activity
is of the same type as the previous activity, and variable #23 reveals whether the
activity considered is of the same type as the next activity. In case of joint activities, if
the previous/next activity of the male/female is different (#16-17 and #23-24), the
order of activity priority is taken into consideration.
Variable #30 reveals the available time of doing non-work activities. If the available
times of the male and female differ, the minimum time available is taken into account
(for joint activities). Variable #31 contains information whether the previous location is
the same as the next location. In case of joint activities, if previous and next locations
of male and female are the same, it is noted as 1 and 0 otherwise. Variable #32 states
about the maximum travel distance of the partner across fixed activities in the same
time window, which is undefined in case of a joint activity (and not used). Variable
#33 is a binary variable indicating whether the car is available for the activity. In case
of a joint activity the maximum is taken. Variables #34-#37 reveal about the maximum
size of the relevant sector in distance bands 1-5, and this is the same for male and
female, since the space-time prisms are defined for the joint case. Variable #38 - #41
define availability characteristics of locations within the (joint) space-time prism.
Variable #38 indicates the nearest band where a location is available and variable #39
the farthest band. Similarly, regarding size, variables #40 and #41 indicate the smallest
and largest size across available locations in the prism. All in all, there are 41 condition
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variables used for deriving the decision tree in case of independent activities, and 39
condition variables are used in case of joint activities (excluding gender and Ptt).
7.6 DECISION TREE INDUCTION AND IMPACT TABLE METHODS
ALBATROSS is a computational process model using a rule-based approach. It
consists of a large number of IF - THEN rules for each choice facet, indicating the
choices made by individuals depend on conditions in terms of socio-demographic
characteristics and other context variables. These rules are extracted from activity-
travel diary data using a tree induction method. Thus, the induction method identifies
the rules that describe which choices are made under which conditions. The basic
algorithm used in ALBATROSS is a CHAID-based tree induction method which
generates non-binary trees. The basic algorithm is appropriate for a categorical action
variable, as implied by the chi-square test statistic that is used as a split criterion. It
generates a decision tree by splitting the condition space on one condition variable at a
time into two or more subsets repeatedly, beginning with the entire data set. The split
that maximizes a significance value of the chi-square test across condition variables is
used for splitting if the split is significant. The process is repeated for each newly
created group until no more significant splits are found. In order to develop the
decision tree, 75% of the cases were used for training and the remaining cases were
used for validation. Readers interested in this topic are referred to Kass (1980).
Decision trees derived from data may become very large and complex and,
consequently, difficult to interpret. This holds true particularly for the present
application where the number of choice observations is very large. Arentze and
Timmermans (2003) developed a method to derive elasticity information from rule-
based models to facilitate interpretation, which we will use here to describe the results
of the tree induction. The method was developed for discrete decision trees, but can be
used with minor adjustment also for a continuous decision tree. The principle of the
proposed method is straightforward. Having derived a rule-based model from the
training data, the model is used to predict for each condition variable the impact on the
action variable. The model is applied to the training set as many times as there are
levels of the condition variable considered. In each run, each training case is assumed
to take on the level of the condition variable considered in that run. The mean and
standard deviation of predictions across training cases under that setting are recorded.
Repeating this process yields a table for each level of the condition variable a
distribution of the action variable defined by a mean and standard deviation. Further
details of this approach can be found in Arentze and Timmermans (2003).
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7.7 DESCRIPTIVE ANALYSIS
Table 7.2 presents the percentages of performing independent activity by male/female
at the same location as the previous activity, the next activity, and at some other
location. Of 49,793 cases, the probability of performing activity at the same location as
the previous activity is 20,710 cases (41.6%) and at the same location as the next
activity is 19,292 cases (38.7%). Meanwhile the probability of conducted activity at
different location as the previous/next activity corresponding to a single stop trip from
a base location (given information available at the scheduling step), is 31,981 cases
(64.2%). In any location choice, females take higher percentage than that of the males.
Nevertheless, in particular in bring/get and daily shopping (shop-1) activities,
male/female is more likely to do multiple activities.
Table 7.3 presents the percentages of performing joint activity by male and female at
the same location as the previous activity, the next activity, and at some other location.
Of 7,150 cases, the probability of performing activity at the same location as the
previous activity is 2,058 cases (28.8%) and at the same location as the next activity is
1,746 cases (24.4%). Meanwhile the probability that the previous location is the same
as the next activity is 5,344 cases (74.7%). As can be seen, male-female tend to do
joint activities with any other joint activity (multiple activities) as well, in particular in
daily shopping (shop-1), social, leisure and touring. Touring takes the highest
percentage in case of the same as the previous location (26.2%). Daily shopping leads
in case of the same as the next location (25.8%). Additionally, social is dominantly
performed when the same as the previous and next activity location (31.7%).
TABLE 7.2 The Percentage of Performing Independent Activity at the Same
Location as Previous and/or Next Activity Current Activity Location =
Previous Activity Location
Current Activity Location
= Next Activity Location
Current Activity Location
≠ Previous/Next Location Activity
Male Female Male Female Male Female
Business 3.0 2.7 3.0 2.3 10.8 7.5
Bring/get 7.4 18.8 7.3 19.1 7.4 12.4
Shop-1 9.0 13.9 9.3 14.0 6.7 9.4
Shop-n 1.3 1.9 1.3 1.9 1.6 2.3
Service 1.7 2.1 1.8 2.2 1.7 2.2
Social 4.0 6.5 3.9 6.5 6.2 9.0
Leisure 4.7 5.9 4.6 5.5 5.7 7.3
Touring 5.6 7.3 5.8 7.7 1.8 2.4
Other 1.4 2.5 1.4 2.5 2.3 3.2
Total 38.3 61.7 38.4 61.6 44.3 55.7
# Cases 7,923 12,787 7,412 11,880 17,811 14,170
Total # cases 20,710 19,292 31,981
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TABLE 7.3 The Percentage of Performing Joint Activity at the Same Location as
Previous and/or Next Activity
Activity Current Activity Location =
Previous Activity Location
Current Activity Location
= Next Activity Location
Current Activity Location ≠
Previous/Next Location
Bring/get 1.8 2.6 2.8
Shop-1 23.5 25.8 21.8
Shop-n 3.9 4.1 5.2
Service 3.0 2.3 5.1
Social 23.4 23.0 34.7
Leisure 18.1 17.4 19.8
Touring 26.2 24.9 10.7
# Cases 2,058 1,746 5,569
TABLE 7.4 The Percentage of Performing Independent and Joint Activity by
Available Distance and Location Size Band in Prisms
Independent Independent Distance (Travel
Time) Male Female
Joint Location
Size Male Female Joint
D1 1.1 0.9 16.9 S1 20.9 21.0 16.9
D2 2.0 1.7 23.7 S2 20.5 20.5 23.7
D3 4.4 4.0 24.2 S3 20.1 20.2 24.2
D4 12.6 11.6 21.1 S4 19.9 19.8 21.1
D5 79.8 81.7 14.1 S5 18.5 18.5 14.1
# Cases 9,075 12,884 22,905 33,728 48,627 22,905
Table 7.4 displays information about the percentages of performing independent and
joint activity by distance bands (the left side) and location size bands (the right side).
The probability of performing independent activity in distance 5 (D5) is the highest
among other distance bands, both for male (79.8%) and female (81.7%). Slightly
different from distance bands, location size shows quite similar results for each band,
in particular in independent activities. The probability of performing independent
activity where a location size is available in prisms, are 20% on average, in particular
from S1 – S3. Surprisingly, the probability of doing joint activity is the same, either
where the distance band is available in the prism or the location size band is available
in the prism.
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7.8 RESULTS
For deriving the independent activities models, a total of 49,793 observations were
derived from the data set. About 75% of these cases were used for training and the
remaining cases were used for validation for each model. For deriving the joint activity
model, a total of 7,150 observations were available in the data set. About 75% of these
cases were used for training and the remaining cases were used for validation of the
model. Table 7.5 shows the decision tree results of the two models of each independent
and joint activity. In this section we will first discuss results for the independent
activities and next consider the joint-activities case.
In the context of independent activities models, the tree generated by CHAID consists
of 67 and 52 leaf nodes (decision rules) for the first and second model. In terms of
goodness-of-fit (hit ratio), model 1 that consists of 3 choice alternatives shows a better
performance than model 2. The hit ratio (based on a probabilistic assignment rule) of
the model compared to a null-model (a root-only decision tree) indicates a modest but
significant improvement: the hit-ratio of a null-model equals 0.484 and the hit ratio of
the tree after splitting equals 0.562. A Chi-square-based contingency coefficient of
0.489 proves that there is a moderately strong impact of the decision tree structure on
the action variable. The overall accuracy on the validation set is almost the same. It
only dropped slightly from 0.562 to 0.551 indicating no overfitting occurs.
TABLE 7.5 Results of Location Decision Tree Models
Independent Activity Joint Activity Indicator
Model 1 Model 2 Model 1 Model 2 N alts 3 25 3 25
N cases 13399 7350 4884 3713
N attr 41 41 39 39
N leafs 67 52 36 23
hit r(0) 0.484 0.041 0.620 0.045
hit r(t) 0.562 0.064 0.702 0.074
hit r(v) 0.551 0.052 0.695 0.066 2χ 4240.27 4237.07 1678.45 2638.80
C 0.490 0.604 0.506 0.645 Note: N alts : number of choice alternatives
N cases : number of observations in training data set
N attr : number of attributes
N leafs : number of leaf nodes 2χ : Chi-Square value
C : contingency coefficient
hit r(0) : expected ratio of correctly predicted cases (null model)
hit r(t) : expected ratio of correctly predicted cases (training set)
hit r(v) : expected ratio of correctly predicted cases (validation set)
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Similar condition also applies to joint activities, where the accuracy on the validation
set is somewhat decreased from 0.702 (training set) to 0.695 (validation set). These fit
measures are even higher compared to the independent activities case indicating that
joint activity models perform substantially better than independent activities models.
Furthermore, the chi-square-based contingency coefficient of 0.506 (model 1) and
0.645 (model 2) also shows a significant and larger impact of the decision rules on the
action variables in the joint case.
Due to limited space and given the large number of decision rules, we cannot display
the entire set of results of the decision tree. Instead, in order to give a summary view of
the outcomes, we will discuss the results of the impact analysis in terms of Chi-square
measures.
7.8.1 Independent Activity
Table 7.6 displays the impact table for independent activity model for the first and
second model. For the first case, there are 3 choice alternatives (action variable),
whether the activity is conducted at the same location as the previous activity (in the
schedule), and the same location of the next activity or at some other location. In case
of a latter decision, a choice from the set of locations available in the prism is made. It
determines the location in terms of a combination of size class and distance class of the
postcode area which involves 25 choice alternatives.
As it appears in model 1 of independent activities, the condition when the previous
location is the same as next location (LvoisLNa) is by far the most important variable
for selecting relative location for conducting an independent activity, as indicated by
the highest Chi-square value. Activity type (Aty), activity duration (Adur), starting time
(Abt), and time availability (Tavail) also have a strong influence in selecting a relative
activity location. Several attributes related to the location, such as order of municipality
(Vogord), travel time from home to previous location (Vohtt), size of area (Vosize), and
previous activity (Voty) have an impact on selecting the activity location.
In addition, characteristics of the next location also have an impact, such as size of
next location (Nasize), travel time from next location to home (Nahtt), order of
municipality of next location (Nagord), and next location is home (NaH). Furthermore,
several personal and household attributes influence the choice of activity location, such
as, gender (Gend), urban size (Urb), Work status (Wstat and PWstat), household
composition (Comp), age (pAge), household income (SEC), day of the week (Day), and
car ownership (Ncar).
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TABLE 7.6 Impact Table for Independent Activity
Model 1 Model 2
No Condition Variable Chi-Square No Condition Variable Chi-Square
1 LvoisLNa 5590,31 1 Vogord 3236,3
2 Aty 3311,57 2 LvoisLNa 2526,3
3 Adur 1958,47 3 Aty 2368,4
4 Vogord 1402,69 4 Comp 178,58
5 Vohtt 670,82 5 Nagord 172,6
6 Vosize 146,49 6 Urb 166,72
7 Gend 21,32 7 SEC 116,38
8 Urb 21,1 8 Vosize 51,18
9 Wodur 17,92 9 Adur 45,78
10 Voty 16,64 10 Vohtt 41,69
11 Nasize 14,76 11 pAge 26,51
12 Wstat 12,59 12 Day 13,37
13 Nahtt 9,52 13 Child 12,99
14 Nagord 6,96 14 Wstat 11,01
15 Comp 3,9 15 PWstat 10,77
16 Abt 3,74 16 Tavail 10,27
17 pAge 3,71 17 Gend 8,98
18 SEC 3,44 18 Vopark 7,49
19 Day 2,16 19 Napark 4,84
20 Ncar 1,75 20 Ncar 2,34
21 PWstat 1,71
22 NaH 1,54
23 Tavail 0,54
In terms of model 2, the number of influential variables appears slightly smaller than
that of model 1 (from 23 to 20 variables). Nevertheless, the significant variables are
almost the same, only the order of priority is somewhat different. In order to choose a
particular time-size band location, the condition when the previous location is the same
as next location (LvoisLNa) and the order municipality of previous location are the
most important variables. Other attributes of the previous activity location still play a
role, such as size of area (Vosize), travel time from home to previous location (Vohtt), and parking price (Vopark). Additionally, several characteristics pertaining to the next
location influencing the decision, such as order of municipality of the next location
(Nagord), and parking price (Napark). Other activity-level components such as activity
type (Aty) and activity duration (Adur) also play a role. The remainder of the influential
variables consists of person and household-level attributes.
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7.8.2 Joint Activity
As shown in Table 7.5, the tree for joint activities consists of 36 and 23 leaf nodes
(decision rules) for the first and second model. Similar to the independent activity
model, the goodness-of-fit of model 1 is also better than that of model 2. The hit ratio
of model 1 compared to a null-model indicates a small but significant improvement:
the hit-ratio of a null-model equals 0.620 and the hit ratio of the tree after splitting
equals 0.702. The overall accuracy on the validation set is also almost the same, fall
somewhat from 0.702 to 0.695. A Chi-square-based contingency coefficient confirms
that there is a strong impact of the decision tree structure on the action variable, in
particular for model 2 (0.645). Although slightly lower than model 1, the result of
model 2 also shows improvement. The hit-ratio of a tree after splitting increases than
that of a null-model (0.074 and 0.045) and there is not much different on the validation
set accuracy (0.066). Overall, the performance of the joint activity model is better than
that of the model for independent activities.
Table 7.7 presents the impact table for the joint activity model for the first and second
model. As it appears, distance maximum in time band (Dmax) and travel time by car
from previous location to home (Vohtt) are by far the most important variables in both
models of joint activity. Other variables at the activity-level play an important role in
deciding on the choice of location for joint activities. Furthermore, other personal and
household-level attributes also influence the location choice decision, such as urban
size (Urb), household income (SEC), driver license holder (Driver), day of the week
(Day), work status of female (PWstat), and car ownership in a household (Ncar).
Similar to the model for independent activities, activity-level attributes also have a
major influence.
To sum, the number of variables influencing the activity location decision for joint
activities is slightly fewer than for independent model. The reason probably the sample
size of independent activities models is fairly larger than joint activities models.
Activity-level attributes have made significant contribution in both the independent and
joint activity models. Activity type (Atype) and activity duration (Adur) in particular,
are significant factors in both models. The tendency of choosing a particular activity
when the previous is the same as the next activity (LvoisLNa) is indicative of a multi-
purpose trip-chain. Moreover, the characteristics of the previous and the next location
also influence the location decision for independent activities. In contrast, distance
maximum in time band (Dmax), is the most influential attribute for doing joint
activities, suggesting that both adults heads prefer to travel jointly for a longer distance.
Furthermore, the travel time (by car) from previous location to home (Vohtt) also has
significant influence on joint activities decision.
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TABLE 7.7 Impact Table for Joint Activity
Model 1 Model 2
No Condition Variable Chi-Square No Condition Variable Chi-Square
1 Dmax 2277.39 1 Dmax 9684.12
2 Vohtt 832.44 2 Vohtt 981.16
3 Aty 354.56 3 Nagord 557.47
4 Adur 157.44 4 Adur 217.77
5 NaH 103.17 5 Dmin 141.91
6 Vosize 88.6 6 NaH 129.32
7 Urb 25.26 7 LvoisLna 37.3
8 SEC 9.26 8 Urb 23.18
9 MxSizeD2 6.02 9 Tavail 22.25
10 Abt 4.81 10 Abt 13.9
11 Vogord 3.52 11 Driver 7.68
12 Day 3.44 12 PWstat 6.97
13 Nagord 1.58
14 VoisA 1.46
15 Ncar 1.03
16 Driver 0.6
Similar to independent activities models, activity duration (Adur) has major impacts in
both models of joint activities. This variable indicates that the decision on choosing a
location depends on activity duration either in independent or joint activity. Overall,
both models have a satisfactory performance. Nevertheless, the performance of the
model for joint activities is better than that of independent activities.
7.9 CONCLUSIONS
This study was intended to refine the ALBATROSS model. In the present paper, we
focus on activity location choice of male-female heads, in particular making a
distinction for those activities that are conducted independently and jointly. Each
model consists of 2 sub-models, where the first model relates to the decision whether
or not the activity is performed in the same location as the previous activity, whether
the activity is done at the same location as the next activity, or whether it is conducted
elsewhere. The second model relates to the last choice option in the first model that
comprises into 25 choice alternatives. It verifies the location in terms of a combination
of size - distance class of the postcode area. The size class depends on a particular
activity type and the size of available facilities at the activity location. Size is classified
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into 5 categories based on employment in the relevant sector for the activity considered
and distance is classified in terms of a detour travel time (by car) also into 5 categories.
For those decisions, rule-based models using a CHAID-based algorithm were derived
from activity-trip diary data. The independent activities models included 67 and 52
condition-action rules for model 1 and model 2 respectively. Although slightly smaller
than the independent activities models, the joint activities models involve 36 and 23
condition-action rules. In both cases, the validity of the decision tree is reasonable in
the sense that the derived rules are readily interpretable and the overall goodness-of-fit
of the model on a validation set is acceptable as well. Furthermore, in both cases, a
substantial improvement in goodness-of-fit relative to a null model indicates that there
is a moderately strong association between condition variables defined at the household,
individual, activity and schedule level, on the one hand, and the location decisions, on
the other. Furthermore, the stability of performance on a validation set suggests that
derived rules are generalizable to hidden cases. These results suggest that the way of
structuring the household decisions as we proposed in this study has merits. Hence, by
refining the existing ALBATROSS in this way we expect that the accuracy and
sensitivity of predictions will be improved.
The tendency of conducting a particular activity at the same location as the previous
activity is higher for independent activities than joint activity. The same condition also
applies to activities that are conducted at the same location as the next activity. These
results imply that male and female are more likely to conduct multiple activities at one
particular location independently than jointly. These results do make sense, since the
activity-travel behavior of one person is different from the other person, even though
male-female couple living in the same household. Moreover, the passion of men is
fairly different from women.
Let’s give you one example. If a man and a woman travelling alone to the shopping
centre zone, they probably have different interests. The man could visit the electronic
shops, watch shops, clothing store (for men), and probably restaurant. From the woman
perspective, she could go to perfume store, jewelry store, beauty center, clothing store
(for women), and perhaps also restaurant. So, if they travel together, they would likely
have less similar purposes. In such cases, they may only go to the restaurant together,
while other activities will be performed alone. Underlying those descriptions, it is
reasonably to say why male or female prefer to conduct multiple activities when they
travel alone than travel together.
Activity-level attributes play a significant role in both the independent and joint
activities models. In terms of independent activities, in any location choice, females
151
take higher percentage than that of the males. Nevertheless, in particular in bring/get
and daily shopping (shop-1) activities, male/female is more likely to do multiple
activities. In terms of joint activities, both adult heads prefer to travel jointly for a
longer distance and that particular joint activity is performed before going home. Both
male and female tend to do joint activities with any other joint activity (multiple
activities), in particular in daily shopping (shop-1), social, leisure and touring.
In addition, activity type and activity duration are significant factors in independent
and joint activities models. The tendency of choosing a particular activity when the
previous is the same as the next activity is indicative of a multi-purpose trip-chain.
Furthermore, the characteristics of the previous and the next location also influence the
location decision for independent activities. In contrast, distance maximum in time
band, is the most influential attribute for doing joint activities, suggesting that both
adults heads prefer to travel jointly for a longer distance. Furthermore, the travel time
(by car) from previous location to home also has significant influence on joint activities
decision.
REFERENCES
Arentze, T.A. and Timmermans, H.J.P. (2000), ALBATROSS: A Learning-based Transportation Oriented Simulation System. EIRASS, Eindhoven University of
Technology, The Netherlands.
Arentze, T.A., and Timmermans, H.J.P. (2003), “Measuring Impacts of Condition
Variables in Rule-Based Models of Space-Time Choice Behavior: Method and
Empirical Illustration”, Geographical Analysis, 35, 24-45.
Arentze, T.A. and Timmermans, H.J.P. (2004), “A Learning-based Transportation
Oriented Simulation System”. Transportation Research Part B, 38, 613-633.
Arentze, T.A. and Timmermans, H.J.P. (2005), ALBATROSS 2.0: A Learning-based Transportation Oriented Simulation System. EIRASS, Eindhoven University of
Technology, The Netherlands.
Arentze, T.A. and Timmermans, H.J.P. (2007), “Robust Approach to Modeling Choice
of Locations in Daily Activity Sequences”. Paper presented in Transportation
Research Board Annual Meeting, Washington, D.C.
Chandraskharan, B. and Goulias, K.G. (1999), “Exploratory Longitudinal Analysis of
Solo and Joint Trip Making in the Puget Sound Transportation Panel”.
Transportation Research Record, 1676, 77–85.
Gliebe, F.J. and Koppelman, F.J. (2002), “A Model of Joint Activity Participation
between Household Members”. Transportation, 29, 49-72.
152
Golob, T., and McNally, M.G. (1997), “A Model of Activity Participation and Travel
Interactions between Household Heads”. Transportation Research B, 31(3), 177-194.
Kass, G.V. (1980), “An Exploratory Technique for Investigating Large Quantities of
Categorical Data”. Applied Statistics, 29, 119-27.
153
Chapter 8
CAR ALLOCATION DECISIONS IN CAR-
DEFICIENT HOUSEHOLDS: THE CASE OF NON-
WORK TOURS
Anggraini, R., Arentze, T.A., and Timmermans, H.J.P., 2010. Paper is officially
accepted for publication in the Journal of Transportmetrica
ABSTRACT The activity-travel decisions of individuals in multi-person households are interrelated. This applies in particular to male-female household heads, as key decision makers in a household. As a result, any realistic model of travel behavior requires accommodating these interpersonal dependencies and household constraints. The present study examines such interactions in the context of the car allocation choice decision in car-deficient households as part of an activity-scheduling process, focusing on non-work tours. A CHAID-based algorithm is applied to derive a decision tree using a large activity diary data set recently collected in the Netherlands. The results show a satisfactory improvement in goodness-of-fit of the decision tree model compared to the null model. The gender seems still to play a role. A descriptive analysis indicates that men more often than women get the car for non-work tours for which a car allocation decision needs to be made. Tour-level attributes are shown to influence the household car allocation decision for non-work tours. The decision to allocate the car is considerably influenced by the longest distance (travel time) from home to a particular location in a tour of men and women. The probability that the men and women get the car increases with the increasing travel time monotonically. Socio-economic and situational factors have less influence to car allocation decision. Overall, men have more influence to the car allocation decision for non-work tour, as indicated by the number of influential variables that relates to the males in the impact table. The developed models will be incorporated in a refinement of the ALBATROSS model – an existing computational process model of activity-travel choice.
154
8.1 INTRODUCTION
Since many decades, mode choice models have received much interest in activity-
based transport demand modeling. Mode choice models are intended to identify which
transport mode people use to go to a particular destination. Virtually all mode choice
models assume that choosing a transport mode is the outcome of an individual decision
making process. However, it has been realized that many activity-travel decisions are
household decisions. Especially, in situations of constrained resources and
synchronization of household activities, models of individual decision making
imperfectly address the decision making between people belonging to the same
household. In car-deficient households, the number of available cars is a scarce
resource, and therefore the decision who will use the car in case of fully or partially
activity-travel episodes in time require a household decision. Although the importance
of household decision making has been realized in activity-based modeling from the
very beginning, models of household decision making are still relatively scarce in this
research community (see Timmermans, 2006 for an overview).
In this paper, car allocation decisions are considered as an element of a more
encompassing activity scheduling process. A large number of factors that potentially
influence car allocation decisions in car-deficient households are considered. These
factors relate to activity-schedule, space-time setting, and individual and household
characteristics. To get a better understanding how a decision is made in a particular
household, we focus on households consisting of two (male-female) heads household.
Both are drivers, and the household owns a single car.
On contributing to the still scarce literature on household activity-travel decisions, this
study will focus on car allocation decisions for non-work tours in car-deficient
households (e.g., more people with a driver license than cars). The paper will report the
conceptualization of the problem and present the empirical results of the car allocation
model for two household heads. In an earlier study (Anggraini, et al., 2008), we
examined car allocation decisions for work tours. In contrast, in this particular study,
we intend to identify how the car is allocated between household heads for non-work
tours. As opposed to work tours, travel for any activity episode or set of chained
activity episodes that does not include a work activity is considered a non-work tour.
The problem of modeling this allocation problem for non-work tours is more complex
than for work tours because the decision at this stage depends considerably on the
outcome of the previous stages in the scheduling process. Hence, the car can be
allocated to the male, female and none.
155
The paper is structured as follows: The subsequent section describes the data used for
extracting the decision trees. The following section explains the methodology: car
allocation model for non-work tours, decision tree induction method and impact table.
After this section, the results of some descriptive analyses will be discussed. This is
followed by a discussion of the empirical analysis of deriving a decision tree from the
MON data. The paper is wrapped up with drawing conclusions.
8.2 DATA DESCRIPTION
The data used originates from the Dutch National Travel Survey (MON = Mobiliteit Onderzoek Netherlands) collected in 2004 covering all of the Netherlands. The survey
is conducted on a regular basis to obtain travel and activity information of residents in
the Netherlands. It is a household survey where data is collected of all household
members for the diary day as well as general information about household and
individual attributes such as gender, age, vehicle ownership and driving license
ownership, home location, individual income, occupation, number of working hours
per week, etc. Respondents were also requested to give information about all trips
made on a designated day as well as on the activities conducted at trip destinations.
Information for each trip includes start time, trip purpose, destination, activity type at
the destination, and transport mode. Situational variables are reported as well. All in all,
this survey provides a comprehensive data source to analyze activity-travel behavior of
Dutch residents. In the data collection, 29,221 households filled out a one-day
travel/activity diary and 28,600 of these households fit the criteria for being considered
in ALBATROSS. The data were transformed to an activity-diary data format for the
current estimation purpose.
8.3 METHODOLOGY
8.3.1 Car Allocation Decisions
The car allocation decision model focuses on car-deficient households (i.e. the number
of drivers exceed the number of cars) and involves a joint decision between male-
female heads. As indicated, the total sample extracted from the MON data includes
28,600 households. Given the purpose of this study, only the following households and
days are relevant: (1) there are two heads in the household; (2) there is one car in the
household; (3) both heads are drivers and (4) both household heads have a non-work
activity on the day considered. As it appears, 3,190 households (and days) and 4,049
number of cases fit these criteria. The model includes the option that none of the
156
household heads uses the car, but some other means of transport instead. Hence, the
decision options are male, female, and none.
As indicated in the introduction, this study is part of a refinement of ALBATROSS
system, an existing operational activity-based model developed for the Dutch Ministry.
ALBATROSS considers several activity types in general: work, work-related such as
business and school, and non-work activities such as escorting, shopping (daily and
non-daily), service-related, social, leisure, and touring. Location decisions for those
activities are made at different moments in the sequential scheduling process. The car
allocation decision to non-work tours is made after the location choice decision. The
decision on car allocation can give significant information for transport mode choice in
the next stage of the process.
As a consequence of the sequential decision process, the available information for each
choice facet is limited. At the moment the car allocation decision for non-work tours is
made, the following information is available. First of all, the generation of a work
activity is executed in the ALBATROSS scheduling process. It includes information
about the number of work episodes, start time and duration, and the location and mode
choice of the work tour. Furthermore, it takes work-related activities (business and
other mandatory) into account. The successive process generates non-work activities,
such as escorting, shopping, service, social, leisure and touring. The first three
activities are considered to be a household task, while the last three activities are non-
household tasks. Household tasks are the type of activity that needs commitment from
household heads, such as delivering children to school. As a result, a task allocation
decision needs to be made.
Joint participation in activities also needs trade-offs between adult heads of household.
This can be applied to either household tasks or non-household tasks. Having defined
all activities in a priority order, trip-chaining is established. Additionally, the location
decision is simulated, and only after all these steps the car allocation decision for non-
work tours is established.
The process underlying the car allocation decision for non-work tours is schematically
shown in Figure 8.1. Primarily, we ensure that only households that are involved in
out-of-home non-work activities are being processed. Those households that do not
conduct any out-of-home activity or only out-of-home work activities will be
eliminated. In addition, only non-work activities performed by male and female are
taken into consideration. Further, only overlapping non-work activities of the male’s-
female’s that occurs in the same time slot is taken into account.
157
Overlapping tours are defined as a pair of tours conducted by respectively male and
female of which the start and/or end times of each tour (simulating use of a car for the
tour) defines a fully or partially overlapping episode. Instead of trip-based, tour-based
concept is assumed to define the car allocation decision between those overlapping
tours.
As we may know, a tour consists of a sequence of trips that starts and ends at a
particular location (i.e., home). We therefore have to determine the primary activity in
each tour. In order to identify the primary activity in a particular tour, we consider the
hierarchical order of activity priority. As mentioned, ALBATROSS considers 10
activity categories in priority order starting from work, business and other (mandatory)
activities. A group of non-work activities is considered, such as escorting, shopping
(daily and non-daily), service-related, social, leisure, and touring. Business and other
(mandatory) activity are also considered as non-work activities in this model.
START
Non-work
tours of both
heads is
overlapping
N
Car Allocation for
Non-Work Tour
Tou
k=1
Allocated to
Male, Female, or
None
k < K
N
STOP
Y
k = k + 1
Y
Non-work
activity is in
HH schedule
Y
N
STOP
STOP
k = index of # car allocation decisions K = # car allocation decisions
FIGURE 8.1 The Process of Car Allocation Decisions for
Non-Work Tour
158
Given the sequential process model underlying ALBATROSS, the car allocation
decision for non-work tours at this stage depends considerably on the outcomes of the
earlier stages in the process, such as work tour decision. A work tour decision is
deemed to have priority and the work tour decision is defined in a previous stage of the
process. Although work tour is an outcast in the present study, nevertheless, it exists as
condition variables, such as number of work activity and work duration (if any) of the
male and female.
To signify how to assign the case of car allocation decisions occurring in a household
on the day concerned, we take the following conditions into account. In principle, only
if the two heads of household having non-work activities it will be taken into account,
and the process is stopped otherwise. Further, only those non-work activity tours that
are performed at the same time period (have an overlap in time) are taken into
consideration. Note that, a single non-work tour of male or female is eliminated from
the process, because at this stage, all activities performed by male and female are
known in the schedule. In addition to that, if the non-work tour of one person is
overlapping with the work tour of the other person, the process is stopped as well,
since the work tour has been assigned earlier. Hence, only fully or partially overlapping
non-work activities are taken into account. In case the primary activity is a joint
activity, the car allocation decision is included also, to decide who the driver is.
In case one person has more than one activity in a tour, the underlying hierarchy of
activity priority is also taken into account to determine the primary activity of the tours.
The decision on who should use a car is executed in this stage. Hence, as mentioned,
the car can be allocated to male, female or none. If a household member is assigned the
car, it means this person is the driver. If the outcome of the model is none, it means that
either household member can still be a car passenger, with another driver than the two
household heads or that both can be non-car users.
Table 8.1 and Figure 8.2 show the case of one particular household that performs a
sequence of activities. As it appears the activity program of the male starts at 06:45 am
(Table 8.1). He further arriving back home at 15:10 pm. During the male’s work
activity, the female goes shopping. The time of conducting these two activities is
overlapping (case #1). However, we ignore such a case, due to the existence of a work
tour. We assume that a car allocation decision is already made for work tours during
this time slot. Further, the female leaves for the work place at 15:30 and is back home
at 18:00 by public transport, while during the same time period (16:30 – 17:30) the
male goes to a sport club. In this case (case #2), non-work tours of male and female are
performed at the same time period.
159
TABLE 8.1 Itinerary of Male-Female Heads in a Particular Household
Activity Time
Male Female
06:45 – 15:10 Work
11:00 – 14:00 Shopping
15:30 – 18:00 Work
16:30 – 17:30 Sport
19:00 – 21:00 Social
As this case is our concern in this study, the decision to allocate the car is necessary to
do. Hence the car allocation decision to non-work tour is executed in this case. After
arriving home, the male makes another tour to meet friend in a restaurant (case #3). In
this case, there are no overlapping episodes and hence, there is no decision for
allocating the car. In summary, among the three cases happened in this particular
household, only one case that is required to be executed, that is case #2.
8.3.2 Decision Tree Induction
CHAID-based tree induction method is used to identify the rules that describe which
choices (i.e., actions) are made under which conditions. CHAID (Chi-square Automatic Interaction Detector) generates non-binary trees, i.e., trees where more than
two branches can be attached to a single root or node, based on a relatively simple
800
1000
1200
1400
1600
1800
2000
2200
600
800
1000
1200
1400
1600
1800
2000
2200
600
Male
Female
Case #1 Case #2
Work Tour
Work Tour
Non-Work Tour
Non-Work Tour
Non-Work Tour
Case #3
FIGURE 8.2 An Example of Defining Car Allocation Decision Cases in
Household Schedules
160
algorithm that is particularly well suited for the analysis of larger datasets. CHAID
relies on the Chi-square test to determine the best next split at each step. CHAID
generates a decision tree by splitting subsets of the space into two or more nodes
repeatedly, beginning with the entire data set (Kass, 1980). The split that maximizes a
significance value of a Chi-square test - after adjustment for multiple testing
(Bonferroni adjustment) - across condition variables is used for splitting if the split is
significant. The process is repeated for each newly created group until no more
significant splits are found. In order to develop the decision tree, 75% of the cases are
used for training and the remaining cases were used for validation. The decision tree
can be interpreted as visualizing a series of decision heuristics that indicate which
decisions will be made (action) under particular situations.
8.3.3 Impact Tables
Decision trees derived from data may become very large and complex and,
consequently, difficult to interpret. This holds true particularly for the present
application where the number of choice observations is very large. Arentze and
Timmermans (2003) developed a method to derive elasticity information from rule-
based models to facilitate interpretation, which we will use here to describe the results
of tree induction. The principle of the proposed method is straightforward. After
having derived a rule-based model from training data, the model is used to predict for
each condition variable a frequency cross table with the levels of the condition
variables in rows and the frequency distribution across the levels of the target variable
(i.e., the action variable) in columns. The frequency table for a given condition variable
is generated by applying the model as many times as there are levels of the condition
variable. In each run, each training case is assumed to take on the level considered on
the condition variable. The frequency distribution across actions of the action variable
predicted under that setting is recorded. Repeating this process for each level of the
condition variable yields a frequency cross table of the condition variable against the
action variable. The impact of the condition variable is then measured as the Chi-
square for this frequency table. Further details of this approach can be found in Arentze
and Timmermans (2003).
8.3.4 Condition and Action Variables
Table 8.2 portrays the condition variables that were used as input to the tree-induction
algorithm. The condition variables concern household-level (including accessibilities),
person-level, tour-level variables, and schedule-level variables.
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TABLE 8.2 Condition Variables for Car Allocation Model
No Level Acronym Variables Classifications
1 HH Urb Urban density 0:most densely , 4:least densely
2 HH Comp Household composition 2:double-1-worker, 3:double-2-worker,
4:double-non-worker
3 HH Child Age of the youngest children 0:no children, 1:<6, 2:6-11, 3:12-17 yrs
4 P Day Day of the week 0:Monday to 6:Sunday
5 P pAge Age of the oldest person in household
0:<35, 1:35-<55, 2:55-<65, 3: 65-<75, 4:75+
yr
6 HH SEC Socio-economic-class (SEC) 0:0-16,250 (low), 1:16,251-23,750 (low-mid),
2:23,751-38,750 (mid-high), 3:38,750+ (high)
7 P wstatm Working status – M 0:non-worker, 1:part-time, 2:full-time
8 P wstatf Working status – F 0:non-worker, 1:part-time, 2:full-time
9 T BTM Start time of doing non-work activity –
M (in hour)
0:≤1030, 1:1031-1333, 2:1334-1620.50,
3:>1620.50
10 T BTF Start time of doing non-work activity –
F (in hour) 0:≤1038, 1:1039-1343, 2:1344-1627, 3:>1627
11 S ntourM # tours in a day – M 1:1, 2:2, 3:3, 4:≥4
12 S ntourF # tours in a day – F 1:1, 2:2, 3:3, 4:≥4
13 S ntourHH # tours in a day – HH 2:2, 3:3, 4:4, 5:≥5
14 S nworkm # work activities in a day – M 0:0, 1:1, 2:≥2
15 S nworkf # work activities in a day – F 0:0, 1:1, 2:≥2
16 S nwohh # work activities in a day – HH 0:0, 1:1, 2:≥2
17 S nNWm # non-work activities in a day – M 1:1, 2:2, 3:3, 4:≥4
18 S nNWf # non-work activities in a day – F 1:1, 2:2, 3:3, 4:≥4
19 S nNWhh # non-work activities in a day – HH 2:2, 3:3, 4:4, 5:≥5
20 T nbusim # business episodes in tour – M 0:0, 1:1, 2:≥2
21 T nbusif # business episodes in tour – F 0:0, 1:1, 2:≥2
22 T nbrm # bring/get episodes in tour – M 0:0, 1:1, 2:≥2
23 T nbrf # bring/get episodes in tour – F 0:0, 1:1, 2:≥2
24 T nsh1m # shop-1-store episodes in tour – M 0:0, 1:1, 2:≥2
25 T nsh1f # shop-1-store episodes in tour – F 0:0, 1:1, 2:≥2
26 T nshnm # shop-n-store episodes in tour – M 0:0, 1:≥1
27 T nshnf # shop-n-store episodes in tour – F 0:0, 1:≥1
28 T nsocm # social episodes in tour – M 0:0, 1:1, 2:≥2
29 T nsocf # social episodes in tour – F 0:0, 1:1, 2:≥2
30 T nserm # service episodes in tour – M 0:0, 1:≥1
31 T nserf # service episodes in tour – F 0:0, 1:≥1
32 T nleim # leisure episodes in tour – M 0:0, 1:≥1
33 T nleif # leisure episodes in tour – F 0:0, 1:≥1
34 T ntoum # touring episodes in tour – M 0:0, 1:≥1
35 T ntouf # touring episodes in tour – F 0:0, 1:≥1
36 T nothm # other episodes in tour – M 0:0, 1:≥1
37 T nothf # other episodes in tour – F 0:0, 1:≥1
38 T distM The longest distance (travel time) in
each tour by car – M (in minute) 0:0, 1:1-6, 2:7-12, 3:13-20, 4:>20
39 T distF The longest distance (travel time) in
each tour by car – F (in minute) 0:0, 1:1-6, 2:7-11, 3:12-20, 4:>20
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TABLE 2 (cont.)
No Level Acronym Variables Classifications
40 T TTptM Travel time ratio between PT & car – M (in
minute) 0:≤100, 1:101-244, 2:245-502, 3:>502
41 T TTptF Travel time ratio between PT & car – F (in
minute) 0:≤100, 1:101-236, 2:237-493, 3:>493
42 T TTcbM Travel time ratio between car & bike – M
(in minute) 0:≤25, 1:26-39, 2:40-100, 3:>100
43 T TTcbF Travel time ratio between car & bike – F (in
minute) 0:≤25, 1:26-40, 2:41-100, 3:>100
44 T NWdurM Duration of non-work tour – M ( in minute) 0:≤52, 1:53-120, 2:121-231, 3:>231
45 T NWdurF Duration of non-work tour – F ( in minute) 0:≤50, 1:51-113, 2:114-216, 3:>216
46 T trainM Train accessibility – M 0:no, 1:yes
47 T trainF Train accessibility – F 0:no, 1:yes
48 T busM Bus accessibility – M 0:no, 1:yes
49 T busF Bus accessibility – F 0:no, 1:yes
50 T rparkM Ratio # paid parking places to total #
parking places – M 0:0, 1:1-7, 2:8-15, 3:16-23, 4:>23
51 T rparkF Ratio # paid parking places to total #
parking places – F 0:0, 1:1-7, 2:8-15, 3:16-24, 4:>24
52 T pparkM Average price of parking – M 0:0, 1:1-7, 2:8-22, 3:23-44, 4:>44
53 T pparkF Average price of parking – F 0:0, 1:1-8, 2:9-22, 3:23-47, 4:>47
54 S mwork Male has work activity in a schedule 0:no, 1:yes
55 S fwork Female has work activity in a schedule 0:no, 1:yes
56 S wdurM Duration of work activity – M ( in minute) 0:0, 1:1-305, 2:306-495, 3:496-560,
4:>560
57 S wdurF Duration of work activity – F (in minute) 0:0, 1:1-245, 2:246-365, 3:366-515,
4:>515
58 S ncarAl # car allocation cases 1, 2, 3, 4, 5
59 T AtourM Primary activity in a tour – M 2:business to 10:other
60 T AtourF Primary activity in a tour – F 2:business to 10:other
61 T nAcToM # non-work episodes in a tour – M 1:1, 2:2, 3:≥3
62 T nAcToF # non-work episodes in a tour – F 1:1, 2:2, 3:≥3
Continuous condition variables, such as travel time, duration, and parking price, are
discretisized by using an equal-frequency interval method which divides a continuous
variable into n parts, in which each part contains approximately the same number of
cases. Household and individual attributes consist of the presence of young children in
a household, work status, socio-economic class (in Euro), household composition, age
of the person, urban density (number of home addresses per area unit in the zone where
the household lives classified on a 5-point scale) and the day of the week (no. 1-8 in
Table 2). Start time of a non-work activity is known at the moment the decision is
made (#9-10). The number of non-work tours performed by male and female in a day
is defined in #11-13. The number of work activity episodes (if any) that is performed
Note: M = Male, F = Female, HH = Household, PT = Public Transport
A = Activity, P = Person, S = Schedule
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by male or female is also taken into account (#14-16). The number of non-work
episodes that is performed by male or female is taken into account (#17-19). The
number of each particular non-work activity in each tour is defined in #20-37.
Accessibility variables, such as travel time, train and bus connections, parking price
and free-paid parking place ratio were also used (#38-43 and #46-53). Note that, all of
those are indicated for the primary activity of the tour. They are calculated based on
national datasets of the transport system (car, bike/walk and public transport), parking
facilities and land-use system (employment data by sector and postcode area). They all
relate to the trip to the non-work location. If a non-work activity is conducted in the
same postcode area as where the person lives, then travel time is set to zero. Travel
time by car is included as a direct measure of accessibility, and refers to the longest
distance (travel time) in each tour if multiple activities are involved. Travel time ratios
between modes are used as indicators of relative accessibility by particular modes.
Ratios are used to allow the algorithm to identify impacts of modes more easily.
Work durations of each male and female (if any) are considered as schedule attributes
(#56-57). Meanwhile durations of non-work tours are defined as tour-level attributes in
#44-45. Variables #54-55 mention whether the male/female has a work activity on a
particular day. Further, variable #58 measures the number of car allocations for non-
work tours in the household. The type of primary activity of a tour is represented in
#59-60. Lastly, the number of non-work activities in each tour is represented in #61-62.
As a result, a total of 62 condition variables are defined. The action variable, the
outcome of the car allocation model, involves assigning the car to the male, female, or
none of the two household heads.
8.4 DESCRIPTIVE ANALYSIS
In this section we describe some descriptive analyses carried out to get a better
understanding of the characteristics of the sample after selecting car deficient
households. As discussed above, only a subset of households is relevant for the car
allocation model, because the problem concerns car allocation to non-work tours in car
deficient households. A total of 3,190 households were selected from the MON data,
yielding 4,049 relevant cases of car allocation decisions.
As mentioned above, with regard to allocating the car to non-work activities, instead of
allocating it for each overlapping activity episodes, we assume it for the overlapping
tours. In this study, tour consists of a group of activity episodes that normally start and
end at home at a particular time.
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TABLE 8.3 Primary Activity of a Tour of Male – Female
No Primary Activity of the Tour Male (%) Female (%)
1 Business 2.69 0.89
2 Bring/get 3.31 3.83
3 Shop-1 24.57 28.06
4 Shop-n 5.63 6.59
5 Service 5.86 5.46
6 Social 23.61 24.50
7 Leisure 21.56 18.40
8 Touring 11.66 10.99
9 Other 1.11 1.28
Total 4,049
TABLE 8.4 Percentage of Getting a Car by Male/Female across Work Status
Male Female Work
Status Use other modes
(%)
Get the car (%) Use other modes
(%)
Get the car (%)
Non-worker 23.59 29.61 55.72 7.78
Part-time 2.79 3.33 15.68 2.52
Full-time 17.19 23.49 15.34 2.96
Total 43.57 56.43 86.74 13.26
1764 2285 3512 537 # cases
4,049 4,049
TABLE 8.5 Average Duration of Non-work Tour(s) across Work Status (in
minute)
Male Female
Work
Status
Average
duration of non-
work tours (min)
Standard
Deviation
(min)
Freq. Average duration of
non-work tours
(min)
Standard
Deviation
(min)
Freq.
Non-worker 160.47 168.79 2154 157.63 162.83 2571
Part-time 197.45 206.64 248 168.22 161.17 737
Full-time 184.76 180.71 1647 171.67 176.04 741
Total 172.62 176.67 4049 162.12 165.10 4049
165
In a single episode tour, the primary activity of the tour is that of the single activity
episode. For example, in a trip-chain of home-shopping-home, it is called a shopping
tour. Nevertheless, in a multi-episode tour, it seems quite complex to define which
activity should be called as a primary activity. Therefore, as said, we defined the
primary activity of the tour based on the hierarchy of activity priority as explained in
Section 8.3.1 and is shown in Table 8.3.
Table 8.3 displays the frequency distribution of non-work tours for which a car
allocation decision is to be made across the primary activity of a tour of the male and
female in the present study. For both male and female, shopping (one store) is the most
frequent activity on the tours that require a car allocation decisions (28.06% and
24.57%). It is followed by social purpose (24.50% for female and 23.61% for male)
and leisure purpose (21.56% and 18.40%). In overall, females have a lower percentage
of business and leisure tours for which a car allocation decision is needed compared to
males and higher percentages of tours for other purposes in the overlapping cases.
Table 8.4 shows the percentages of getting a car for non-work tours by male and
female across work status. The probability of the male getting the car is 56.43% and
female that gets a car is about 13.26% across 4,049 cases. In terms of work status, in
29.61% and 23.49% of the cases where a car allocation decision is involved the male is
a non-worker and full-time worker respectively, and gets the car. Meanwhile, in
55.72% of the cases where a car allocation decision is involved the female is non-
worker and uses another transport mode. The result indicates that male more often than
female uses a car in an overlapping non-work tour.
Table 8.5 shows the distribution of the average non-work tour(s) duration of male and
female by work status. Note that persons may conduct more than one non-work tour in
a day, and the figures presents the durations on a per-tour basis including the travel
time. As we can see, on average males and females have similar non-work tour
durations when a car allocation decision is involved. However, in each work status
group, the average duration of the males is higher than that of the females.
8.5 RESULTS
As indicated, for deriving the car allocation for non-work tours model, a total of 4,049
observations could be derived from the data set. About 75% of these cases (3,057)
were used for training and the remaining cases were used for validation. Given a
minimum group size of n=50 cases at parent nodes and a 5% alpha level, the tree
generated by CHAID consists of 28 leaf nodes (decision rules).
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TABLE 8.6 Results of the Car Allocation Model to Non-Work Tours
Indicators Results
N alts 3
N cases 3,057
N attr 62
N leafs 28
hit r(0) 0.432
hit r(t) 0.544
hit r(v) 0.526
2χ 1,040.25
C 0.504
Note: N alts : number of choice alternatives
N cases : number of observations in training data set
N attr : number of attributes
N leafs : number of leaf nodes
hit r(0) : expected ratio of correctly predicted cases (null model)
hit r(t) : expected ratio of correctly predicted cases (training set)
hit r(v) : expected ratio of correctly predicted cases (validation set) 2χ : Chi-Square value;
C : contingency coefficient
TABLE 8.7 Impact Table of Car Allocation Decision to Non-Work Tour Model
No Variables IS ISmale ISfemale ISnone MSmale MSfemale MSnone
1 distM 2181,52 955.52 675.36 550.65 1.00 -0.99 -1.00
2 distF 493,19 4.39 271.27 217.53 -0.23 1.00 -1.00
3 AtourM 146,72 30.20 17.95 98.57 -0.87 -0.41 0.71
4 ntouf 37,19 9.53 2.48 25.19 -1.00 -1.00 1.00
5 TTcbF 30,22 1.38 13.20 15.65 -1.00 -1.00 1.00
6 ntoum 29,05 11.10 0.02 17.93 -1.00 1.00 1.00
7 AtourF 14,96 2.55 2.19 10.21 0.00 0.00 0.00
8 Urb 12,74 2.39 1.76 8.60 1.00 1.00 -1.00
9 RParkM 12,19 4.50 0.51 7.18 -1.00 -0.04 1.00
10 nsh1m 11,2 4.59 0.60 6.01 0.00 0.00 0.00
11 nNWm 8,44 0.39 3.54 4.50 0.00 0.00 0.00
12 nNWf 7,91 0.57 6.59 0.74 -0.87 0.37 0.02
13 nAcToF 4,06 0.13 1.91 2.01 0.00 0.00 0.00
14 TrAcM 3,56 1.48 0.31 1.77 1.00 -1.00 -1.00
15 pAge 3,49 0.62 2.87 0.00 -0.33 0.33 -0.33
16 NWdurM 2,34 0.85 1.21 0.28 1.00 -1.00 -1.00
17 BTM 1,95 0.79 0.08 1.07 -1.00 1.00 1.00
18 nleim 1,94 0.79 0.08 1.08 -1.00 1.00 1.00
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The hit ratio (based on a probabilistic assignment rule and the training set) of the model,
compared to a null-model (a root-only decision tree) indicates a significant
improvement achieved by the tree: the hit-ratio of the null-model of 0.432 is
significantly increased to 0.544, as shown in Table 8.6. The overall accuracy on the
validation set is almost the same, only dropped slightly to 0.526 indicating that no
overfitting occurs. A Chi-square-based contingency coefficient of 0.504 confirms that
there is a moderately strong impact of the decision tree structure on the action variable.
To evaluate the quantitative impacts of each condition variable on the action variable,
Table 8.7 displays the impact table of the car allocation model for non-work tours. The
condition variables are listed in order of decreasing impact on the action variable
overall (the IS column). Note that ISmale, ISfemale, and ISnone show the size of the impact
for each action separately. The last three columns (the MS column) identify the
monotonicity measure of the condition variable across the action variable. The
condition variable that has a monotonically increasing impact on the frequency of a
particular action variable across the levels of the condition variable signify MS equals
1 and otherwise -1 if it has a monotonically decreasing impact. Any value in between
these extremes indicates that the impact is nonmonotonous in the direction indicated by
the sign across the range of the condition variable.
When we look at the differential impacts of types of condition variable, we see that the
tour-level variables have the strongest impact. The variable that gives by far the biggest
impact is the longest distance (travel time) from home to a particular destination in a
tour by male and female (distM and distF). The monotonicity measure (MSmale = 1.00
and MSfemale = 1.00) clarifies that with the increasing travel time, the probability that
the male and female get the car increases monotonically.
The monotonicity measure for the variable that gives the next biggest impact is the
primary activity of a tour performed by male (AtourM) in a day. It indicates that as the
activity code (from 2 to 10) goes up, the probability of none of the heads using the car
increases nearly monotonically (MSnone = 0.71). Note that an increase of activity code
means approximately a transition from mandatory to a discretionary nature of the
activity. The next variable that gives a significant impact is the number of touring
activities performed by the female in a tour (ntouf). It shows that an increasing number
of touring episodes decreases monotonically the probability that the male or female
uses the car (MSmale = -1 and MSfemale = -1). In addition to that, several other variables
at the tour-level that gives influence to the car allocation decision are the number of
activity episodes in each tour, such as touring, shop to multi store, and leisure episodes
performed by male (ntoum, nsh1m, and nleim). The primary activity of the tour
performed by female (AtourF), the number of activity episodes in a tour of female
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(nAcToF) and start time of doing non-work activities by male (BTM) are also
influential tour-level variables. As such, the number of non-work episodes of male and
female in a day (nNWm and nNWf) and the duration of non-work tour of male
(NWdurM) also have an influence on the car allocation decision.
In terms of situational variables, we find that the most outstanding variable is
urbanization (Urb). The increasing level of urbanization variable, which indicates a
decreasing level of urban density, the tendency of male or female getting the car
increases monotonically (MSmale = 1 and MSfemale = 1). As for socio-demographic
variables, the only influential variable is the age of oldest head (male/female) in
houseshold. The probability of getting the car increases non-monotonically for the
female (MSfemale = 0.33) and decreases non-monotonically for the female (MSfemale =
0.33) with increasing age of the oldest head.
In relation to accessibility measures, there are some variables that have significant
impact to the car allocation decision. Those are travel time ratio between car and bike
of the female (TTcbF), ratio # paid parking places to total # parking places by male
(RParkM), and accessibility of train in the location of male (TrAcM).
8.6 CONCLUSIONS
This paper considered car allocation choice behavior in car-deficient households
explicitly in the context of an activity-scheduling process. Focusing on non-work
activities, a car allocation model based on rules derived from a large travel diary data
set using a CHAID-based induction algorithm was presented. The face-validity of the
decision tree model is good in the sense that the derived rules and impacts of condition
variables are readily interpretable. The overall goodness-of-fit of the model is
satisfactory. Although the performance on a validation set decreased slightly, the set of
decision rules seems stable across training and validation set to a satisfactory extent.
In 56.43% of the car-allocation decision cases men gets the car while in only 13.26%
of the cases women use the car. In the remaining cases (30.30%), none of the heads
uses the car. With regard to work status, in 53.10% of the cases where a car allocation
decision is involved, the male is a non-worker or full-time worker and uses the car.
Meanwhile, in 55.72% of the cases, the female is non-worker and uses another
transport mode. The result indicates that men more often than women in gets the car
when their non-work tours overlap. This result is almost similar to our previous study
on car allocation decisions for work tours (Anggraini, et al., 2008), where we found
169
that the probability of the men getting the car to work location is also higher than that
of the women.
In terms of the activity on the tour, the percentage of female and male doing shopping
(to one store) tour is the highest among other activities in car-allocation cases. It is
followed by social tour (24.50% for female and 23.61% for male) and leisure tour
(21.56% for male and 18.40% for female).
In terms of decision rules, as the impact table analysis showed, longest distance (travel
time) from home to a particular destination in a tour of male and female have an
important role in car-allocation decisions to non-work tours in two-driver, single-car
households. It indicates that the probability that the male and female get the car
increases with increasing travel time to a destination location. In addition to that, some
other variables that relates to activity-level have significant influence to the decision of
allocating the car. Those variables are the primary activity of a tour performed by male
and female in a day, and the number of episode of a particular activity such as touring,
shop to multi store, and leisure episodes performed by male and female in a tour. The
start time of doing non-work activity by male and its duration also have great influence
to car allocation decision. The probability of the male to get the car tends to decrease
monotonically when the start of doing non-work activity increases. This result is in
contrary to that of the females. Female’s probability to get a car increases when the
male starts doing non-work activity later. Overall, men have more influence on the car
allocation decision for non-work tours, as indicated by the number of influential
variables that relates to the male in the impact table.
Socio-economic variables have less influence on car allocation decisions, only age of
the person has an impact. The increase of the age of the oldest person in the household
will increase slightly the probability of women to get the car. Situational variables also
have less influence on the decision of allocating the car. The most influential variable
in this class is urban size, where the increasing level of urban size (from the most
densely to the least densely areas) will also significantly increase the probability of the
men and women to get the car. Slightly different from those two variable types,
accessibility measures variables have moderate influence to car allocation decision.
In general, the results indicate that gender still plays a role in car-deficient household’s
car allocation decisions. Men are still prevailing women in getting the car to non-work
tour. The decision from car allocation obviously is informative for transport mode
choice in the subsequent choice facet in the scheduling process. Hence, the car
allocation decision to non-work tour has proved that the model can be included as one
element in an activity scheduling process as what ALBATROSS done. ALBATROSS
170
proved to be a suitable framework for this. By understanding the car allocation
decision in car-deficient households, the mobility of the household members can also
be recognized well.
REFERENCES
Anggraini, R., Arentze, T.A., and Timmermans, H.J.P. (2008), “Car Allocation
between Household Heads in Car Deficient Households: A Decision Model”.
European Journal of Transportation and Infrastructure Research, 8(4), pp. 301-319.
Arentze, T.A. and Timmermans, H.J.P. (2000), ALBATROSS: A Learning-based Transportation Oriented Simulation System. EIRASS, Eindhoven University of
Technology, The Netherlands.
Arentze, T.A. and Timmermans, H.J.P. (2003), “Measuring the Goodness-of-Fit of
Decision-Tree Models of Discrete and Continuous Activity-Travel Choice:
Methods and Empirical Illustrations”. Journal of Geographical Systems 5: 185-206.
Arentze, T.A., and Timmermans, H.J.P. (2003), “Measuring Impacts of Condition
Variables in Rule-Based Models of Space-Time Choice Behavior: Method and
Empirical Illustration”, Geographical Analysis, 35, 24-45.
Arentze, T.A. and Timmermans, H.J.P. (2004), “A Learning-based Transportation
Oriented Simulation System”. Transportation Research Part B, 38, pp.613-633.
Arentze, T.A. and Timmermans, H.J.P. (2005), ALBATROSS 2.0: A Learning-based Transportation Oriented Simulation System. EIRASS, Eindhoven University of
Technology, The Netherlands.
Kass, G.V. (1980), “An Exploratory Technique for Investigating Large Quantities of
Categorical Data”. Applied Statistics 29, 119-27.
Timmermans, H.J.P. (2006), Analyses and Models of Household Decision Making
Processes. In: Proceedings of the IATBR Conference, Kyoto (CD-ROM, 34 p.p).
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Chapter 9
THE INTEGRATED MODEL
9.1 INTRODUCTION
ALBATROSS is a comprehensive activity-based model of transport demand, which
accounts for multiple facets of activity-travel behavior. As indicated, the aim of this
PhD project is to more systematically incorporate household decision making in the
model, replacing the earlier sequential approach. To that effect, based on a slightly
different scheduling process model, a new set of decision rules were derived from
empirical data. To judge the added value of this endeavor, ideally the heuristics
underlying the model should be compared. In that context, the model uses
individual/household decision trees that can be interpreted as decision rules to
predict/simulate a complete activity-travel pattern for each household head in a
synthetic population. Unfortunately, it is not possible to compare the model results at
the level of decision trees between the existing and new version because of the
involved change in the structure of the scheduling process. However, it is possible to
compare results based on the generated complete activity schedules; that is, on the
level of the integrated model.
In this chapter, we therefore evaluate the validity and sensitivity of the full, integrated
model in two respects. First, as a test of validity, we evaluate the extent to which the
172
model is able to reproduce observed frequency distributions and mobility indicators in
the MON dataset. We do not expect dramatic differences in that respect because both
versions derive the rules from the same data set. We expect that the new model is able
of reproducing the aggregated distributions as well as the existing model. Second, we
apply the model to a particular scenario of change in the Dutch population to evaluate
the sensitivity of the model. We apply both the existing and new version of the model
to a scenario using a synthetic population of the Netherlands. On this level, we do
expect a difference between the existing and new model. That is, we expect that the
new model will predict different effects due to the fact that it better takes into account
within-household interactions. The sensitivity analysis is also performed on the basis
of a set of relevant frequency and indicator variables of behavior.
The chapter is organized as follows. First, the results of the validity test using the
MON data will be reported. Predicted and observed aspects of activity-travel pattern
will be compared between the different versions of the model at the activity, tour and
schedule level. In addition, comparisons will be based on a set of performance
indicators that the model generates. Next, the sensitivity of the new version of the
model is compared to that of the old models, based on a scenario of increased
participation of women in the workforce. The chapter is completed with conclusions
and discussion.
9.2 TEST OF VALIDITY USING MON DATA
In order to test the performance of the comprehensive ALBATROSS model system,
we compare the goodness-of-fit of the predictions of the old and new version of the
integrated model on the Dutch National Travel dataset (MON). The old version uses
individual-based decisions and the new version uses household-based decisions where
appropriate. We expect that the result of the new version is not really different from
the old version in terms of frequency distributions and indicators. Both models should
be able to reproduce the aggregate distributions that are found in the MON data. In this
section, we compare the goodness-of-fit between the models.
9.2.1 Frequencies
To examine the validity of the model, the discrepancy between observed and predicted
data of the old version and new version of ALBATROSS system is analyzed. The Chi-
square ( 2
iχ ) measure can be used as a measure of difference between an observed and
predicted frequency distribution. It is defined as follows:
173
[9.1]
where i is an index of cell of the frequency table. Three Chi-square measures are used
to identify the differences between: (i) observed and predicted data of the old version
( 2
1χ ), (ii) predicted data of the old and new versions ( 2
2χ ), and (iii) observed and
predicted data of the new version ( 2
3χ ). As for (i) and (iii), the smaller the difference
the better the performance of the model is. Analysis (ii), on the other hand, indicates a
difference between the two models.
Table 9.1 displays the results of a frequency analysis of activity patterns generated by
ALBATROSS model for the MON sample for both the individual-based (old version)
and household-based (new version) decisions. The table illustrates the observed
frequencies in the MON data and predicted frequencies by the old version and new
version of ALBATROSS in terms of some variables. The variables shown here
represent the most relevant facets at the activity-level, tour-level, and schedule-level. Activity-level facets refer mainly to all main activity attributes. The frequency
distribution across activity types is fairly accurate. The discrepancy stays within a
range from 0 – 2 percent points. The only clear tendency in both models is that the
frequency of work activities is somewhat underpredicted. This is a known bias that is
due to the fact that ALBATROSS imposes the restriction of maximally two work
episodes per person. The Chi-square value that measures the discrepancy between
observed data and prediction of the new version ( 2
3χ = 145.32) proves a low
dissimilarity (given the large sample size we have), meaning that the new version
model predicts the activity type distribution accurately. Furthermore, the accuracy is
even somewhat better than the old version ( 2
3χ = 145.3 versus 2
1χ = 232.8). The time-
of-day distribution displays a relatively high dissimilarity which is caused by a shift
from day-time time slots to evening. Also, this bias is known and has been reported
before. The temporal constraints imposed on schedules cannot be fully accounted for in
the decision trees so that during scheduling a certain proportion of activities are shifted
from blocked to open time slots where open time slots are more likely to occur in the
evening. The old version and new version show similar predictions, as indicated by a
relatively low Chi-square value ( 2
2χ = 526.06). The bias is slightly stronger in the new
version as indicated by the higher value of the Chi-square. The explanation for this is
that joint activities have a higher probability to find an only feasible time slot in the
evening compared to independent activities, as they have to meet temporal constraints
of both persons at the same time, resulting in a somewhat larger shift.
∑−
=i
iii
ected
ectedobserved
exp
)exp( 2
2χ
174
TABLE 9.1 Some Relevant Variables at the Aggregate Level
Predicted Data (%) Activity Type
Observed
Data (%) Old Version New Version
Work 20.47 18.88 18.39
Business 5.80 6.43 5.79
Bring-Get 7.96 8.30 8.65
Shop-1 store 20.92 22.53 21.61
Shop-n store 4.07 4.46 3.97
Service 5.28 5.65 5.08
Social 13.17 11.67 13.86
Leisure 12.90 12.51 12.92
Tour 8.04 8.16 8.27
Other 1.39 1.41 1.47
Total 82,584 76,842 78,812 2
1χ = 232.83; 2
2χ = 250.29; 2
3χ = 145.32
Activity Time of Day
<=10 am 29.03 25.79 24.69
10-12 am 16.71 13.99 12.88
12-2 pm 15.23 12.93 11.96
2-4 pm 15.63 16.72 15.01
4-6 pm 9.84 11.84 12.29
> 6 pm 13.55 18.73 23.16
Total 82584 76842 78812 2
1χ =1334.11; 2
2χ = 526.06; 2
3χ = 3267.36
Trip-Chain Pattern
Single-stop 63.61 63.25 69.45
After-stop 13.44 15.54 13.06
Before-stop 13.44 15.54 13.06
In-between stop 9.52 5.67 4.43
Total 82584 76842 78812
2
1χ = 1015.18; 2
2χ = 678.59; 2
3χ = 1700.95
Activity Location
Home Zone 30.059 32.107 36.251
Home Municipality 29.475 25.797 25.043
Municipality order1 14.876 16.300 14.867
Municipality order2 9.094 10.367 9.288
Municipality order3 5.843 6.195 5.834
Municipality order4 3.816 4.110 3.700
Municipality order5 5.054 5.125 4.582
Total 82584 76842 78812
2
1χ = 1754.39; 2
2χ = 684.59; 2
3χ = 1430.83
175
TABLE 9.1 (cont.)
Predicted Data (%) First Tour Mode
Observed
Data (%) Old Version New Version
Car 43.31 47.31 44.55
Slow 42.86 38.09 40.23
Public 3.10 3.15 3.14
Car Passenger 10.73 10.66 11.31
Total 63627 60544 65027
2
1χ = 791.33; 2
2χ = 101.02; 2
3χ = 568.02
Number of Activity in a Tour
1 82.56 80.27 84.17
2 10.19 14.55 12.07
3 4.55 3.71 2.66
4 1.48 1.03 0.73
> 4 1.22 0.43 0.38
Total 63627 60544 65027
2
1χ = 831.31; 2
2χ = 348.20; 2
3χ = 887.54
Number of Tour in a Schedule
0 18.10 21.81 19.04
1 45.41 42.18 42.85
2 24.38 24.48 24.38
3 8.52 8.34 9.19
> 3 3.59 3.18 4.55
Total 46876 46593 46593
2
1χ = 229.46; 2
2χ = 221.47; 2
3χ = 109.83
Number of Non-Work Activity in a Schedule
0 31.58 34.94 31.90
1 30.69 28.60 32.17
2 19.24 17.99 17.75
3 9.91 9.88 9.43
4 4.84 5.16 4.75
> 4 3.74 3.42 3.99
Total 46876 46593 46593
2
1χ = 144.63; 2
2χ = 195.79; 2
3χ = 54.84
176
In terms of trip-chaining, both models predict the frequencies of the so-called After
stops and Before stops rather accurately but underpredict the Between-stops somewhat.
The underprediction is slightly bigger in case of the new model. Also, this can be
understood in terms of the increased difficulty of finding a feasible in-between time
slot due to additional constraints that joint activities bring along.
The last variable that is taken into account in this class is activity location. The activity
location that is the same as the home zone (Home Zone) is slightly overpredicted by
both models and a little more so by the new model. The frequency of other location
types (outside the home zone and within own municipality and outside the home
municipality in municipalities of different order) are predicted accurately. Again, this
slight difference between the old version and new version can be attributed to increased
constraints that joint activities must meet compared to independent activities. All in all,
the new version predicts location type frequencies slightly better than the old version
( 2
3χ = 1430.83 versus 2
1χ = 1754.39).
At the tour-level, a first variable considered is the transport mode of the first link of the
tour. Here, the old version and new version shows a similar prediction as indicated by a
low value of the Chi-square measure ( 2
2χ = 101.02). However, the prediction of the new
version seems somewhat better than the prediction of the old version as indicated by a
lower Chi-square value ( 2
3χ = 568.0 versus 2
2χ = 791.3). In terms of number of activities
in a tour, the two models perform approximately equally ( 2
3χ = 887.5 versus 2
2χ =
831.3). In both cases there is a slight underprediction of the multiple-activities tours
that might be related to the (imposed) underprediction of work activity episodes.
At the schedule-level, the prediction of the new model in terms of the number of tours
in a schedule shows a satisfying result ( 2
3χ = 109.83). It accurately predicts the
frequency distributions of schedules across numbers of tours on a day. Compared to
the old model the prediction is even more accurate. Finally, regarding the number of
activities in a schedule, the new model also shows an improvement in accuracy of the
prediction as indicated by the lower Chi-square value ( 2
3χ = 54.8 versus 2
2χ = 144.6). In
overall, the new model shows equal or better predictions in the frequency distributions
of the relevant variables, than the old model, except for time of day and trip-chaining.
9.2.2 Indicators
In addition to frequency distributions of relevant variables, we also calculated a set of
relevant indicators to examine the validity of the model. Again, we use the MON
177
sample and evaluate the dissimilarity between observed and predicted data of the old
version and new version of ALBATROSS. We consider the system total as well as the
mean across schedules, standard deviation, difference in means and t-value of
differences in means for each indicator. The significance of differences between means
is based on a two-sided independent samples t-value. The t-value is defined as follows:
[9.2]
where
Similar to what we did in case of frequency analysis, there are three aspects that we
want to compare. The three t-values are used to identify the differences between: (i)
observed and predicted data of the old version (t-value1), (ii) predicted data of the old
and new versions (t-value2), and (iii) observed and predicted data of the new version
(t-value3). As for (i) and (iii), smaller differences indicate better performance of the
model. Analysis (ii), on the other hand, indicates a difference between the two models.
Table 9.2 displays the observed values in the MON data and predictions of the old and
new version of ALBATROSS for the same sample in terms of a number of indicators
that are generally of interests. In terms of total travel time, the old and new model
show small differences in prediction, as signified by a low t-value (t-value2 = -2.1).
Although both models show an underprediction of average travel times (which is
known to the developers), the new model seems somewhat better than the old model
(t-value3 = 41.3 and t-value1 = 42.3). In terms of travel time for each mode, the new
version of ALBATROSS shows a better prediction than the old version for all
transport modes, except car driver. The prediction of number of tours and number of
trips by the new model are also more accurate than the old model. In terms of distance
traveled by each transport mode, the new model’s predictions are good for every
transport mode except slow modes (the latter is mainly due to some outliers of very
long slow mode travel times in the MON data). Only in terms of total distance, the
prediction of the old version is slightly better than the new version. Overall, for most
indicators, the new model performs slightly better than the old model.
2
2
2
1
2
1
21
n
S
n
S
XXt
+
−=
1X is the mean of sample 1 n1 = size of sample 1
2X is the mean of sample 2 n2 = size of sample 2
2
1S is the variance of sample 1
2
2S is the variance of sample 2
178
9.3 TEST OF SENSITIVITY
Another and perhaps more interesting test is whether the potentially improved
decisions mechanism at the household level make the model more sensitive to evaluate
policy scenarios that should be expected to influence household decision making. The
results of such a test are described below.
9.3.1 Synthetic Population
ALBATROSS has been developed to analyze the impacts of possible scenarios on
activity patterns and related travel demand. To that end, first a synthetic population
needs to be constructed for the whole Netherlands. The synthesis agent uses two sets
of data, namely national population statistics by zone (1308 zones) and a national
sample of households. The population statistics define the marginals and the sample
data the initial proportions of a multiway household attribute table that is generated
and fitted using an iterative proportional fitting method (IPF). Generated populations
by zone (1308 zones) are then allocated to the post code areas (3987 areas) within the
zone proportional to the known population sizes in postcode areas (Arentze and
Timmermans, 2005).
The results of the population synthesis procedure replace existing observed schedules.
The new set of observed schedules specifies for each case the day of the week, an
empty schedule for each person-day and household and person level data. It should be
noted that the synthesis module takes much computation time, because an IPF
procedure is run 1308 times, each for each zone, and involves fitting the data on both a
household and person level.
9.3.2 Scenario
In order to evaluate the sensitivity of the new ALBATROSS model, we develop a
scenario on the level of the synthetic population. The scenario considered here
involves an increase of women participation in the labor force of 65 % overall (labor
scenario) assuming the year 2000 as the base year. The scenario considered here
involves an increase of women participation in the labor force of 65 % overall (labor
scenario) assuming the year 2000 as the base year. The increase of 65% of labor
participation of women led to an increase of 41% of labor participation of women
household heads (apparently the increase is stronger for women that are not household
heads). So, this relates to women that are household heads and hence, occur in the
179
ALBATROSS population. This is a relatively strong increase, but it should be noted
that, in the scenario, the labor participation rate of women is still substantially less than
that of men. The ratio of part-time workers was not changed in the scenario meaning
that a much larger proportion of working women are part-time workers compared to
men. Due to correlations, the scenario population will also display differences in other
socio-demographic characteristics. Table 3 shows the differences between the baseline
and labor scenario for household composition, presence and age of children, car
possession, age of person, and work status of person. As side effects, there are shifts
towards higher income levels, no children in the household and an increase in car
possession. There are no noticeable differences in age distribution as age is a variable
that is constrained by population data in the synthesis. The differences between
populations will be discussed in more detail below.
To identify predicted effects, we compare the prediction under the scenario with the
prediction of the baseline for each of the two model versions. The results are displayed
in Table 9.3 and Table 9.4 for the old and new version respectively. Comparison with
the baseline reveals the effects of the scenario that each model predicts. In turn,
comparison of predicted effects between models reveals the extent to which the
models differ. An increase of sensitivity of the new model would emerge as a
difference in predicted effects. This means that a difference in predicted effects is
evidence for an improved sensitivity of the model (and a better prediction). We use the
same set of attributes and indicators as before for this analysis. Furthermore, we use a
standard functionality of ALBATROSS to reveal the variance of stochastic variation in
predictions. For each prediction, ALBATROSS calculates the mean and standard
deviation between subsets of the set of predicted schedules. The subsets are
determined based on a random partitioning of the set (in three subsets). Each table
shows information about the mean across subsets of the base-scenario (m0), difference
in means between base-scenario and labor-scenario as a percentage of m0 (m1-m0 %)
and the t-value of differences in means for each variable/indicator. The significance of
the differences between means is based on a two-sided, independent samples t-test.
Degrees of freedom are corrected for possible differences between the two
distributions. The significance of difference of means on a variable/indicator is
represented by a star symbol, where one star indicates significance at 5% alpha level
and two stars significance at the 2.5% level.
Table 9.3 illustrates the comparison between baseline and labor scenario in terms of
socio-demographic variables. As expected, with the increasing labor participation of
women, single-1-worker and double-2-worker households increase significantly with
14.32% and 32.67% respectively. With regard to household SEC (income), medium
and high income households increase with 3.15% and 7.19%, respectively.
180
TABLE 9.2 Observed and Predicted of the Old and New Versions
OBSERVED PREDICTED (OLD VERSIONS) PREDICTED (NEW VERSIONS) INDICATORS
Total N Mean Stdev Total N Mean Stdev Total N Mean Stdev t-value1
t-value2
t-value3
Total travel time 2997661 46876 63.9 71.0 2128941 46593 45.7 60.8 2167099 46593 46.5 57.4 42.3 -2.1 41.3
Travel time car
driver 1466223 46876 31.3 54.8 1085816 46593 23.3 40.7 1051020 46593 22.6 38.8 25.3 2.9 28.1
Travel time
public 249738 46876 5.3 30.8 210930 46593 4.5 28.1 215128 46593 4.6 27.4 4.2 -0.5 3.7
Travel time slow 873736 46876 18.6 39.5 601229 46593 12.9 38.6 633901 46593 13.6 35.1 22.4 -2.9 20.6
Travel time car
passenger 407964 46876 8.7 31.0 216631 46593 4.6 17.4 254214 46593 5.5 19.1 24.7 -6.7 19.3
Number of tours 63585 46876 1.4 1.0 60521 46593 1.3 1.0 65027 46593 1.4 1.1 8.3 -13.7 -5.7
Number of trips 146115 46876 3.1 2.4 137145 46593 2.9 2.4 143839 46593 3.1 2.5 11.1 -8.9 1.9
Ratio trips-tours 2.29795 2.26607 2.21199
Ratio single stop
tours - all tours 0.82559 0.80441 0.84167
Total travel
distance 1812815 46876 38.7 82.4 1632628 46593 35.0 64.6 1622956 46593 34.8 61.8 7.5 0.5 8.1
Distance car
driver 1115245 46876 23.8 62.7 1200032 46593 25.8 57.0 1138415 46593 24.4 53.6 -5.0 3.7 -1.7
Distance car
passenger 331312 46876 7.1 35.4 226063 46593 4.9 23.3 277702 46593 6.0 26.1 11.3 -6.9 5.5
Distance slow 223210 46876 4.8 34.3 79349 46593 1.7 6.9 80604 46593 1.7 6.5 18.9 -0.6 18.8
Distance public 143048 46876 3.1 24.2 127184 46593 2.7 24.1 126235 46593 2.7 22.5 2.0 0.1 2.2
181
TABLE 9.3 Comparison between Base-line and Scenario on Socio-Demographic
Characteristics
Household Composition m0 m1-m0 (%) sign t-value df
Single, 0-worker 52134 -11.81 ** -31.889 4
Single, 1-worker 42460 14.32 ** 88.292 3
Double, 1-worker 39852 -27.73 ** -104.422 3
Double, 2-worker 60545 32.67 ** 62.848 3
Double, 0-worker 32931 -26.2 ** -96.458 3
Total 227922 0.01 0.07 4
Household SEC
Minimum 61086 -7.45 ** -18.438 3
Low 55251 -2.7 ** -8.345 3
Medium 48576 3.15 ** 5.778 4
High 63009 7.19 ** 95.832 3
Total 227922 0.01 0.07 3
Presence and Age of Children
No child 164326 0.74 ** 6.743 4
< 6 yr 29190 -3.54 ** -5.845 4
6-<12 yr 17936 -1.58 ** -10.251 3
12-<17 yr 16470 0.71 1.013 2
Total 227922 0.01 0.07 3
Number of Cars in Household m0 m1-m0 (%) sign t-value df
No car 46327 -5.26 ** -9.764 4
One car 127312 -0.74 * -2.355 3
2 or more 54283 6.27 ** 20.098 3
Total 227922 0.01 0.07 3
Age of Person
< 35 yr 83562 -0.32 -0.697 2
35-<55 yr 156184 0.58 * 2.662 4
55-<65 yr 51397 1.56 ** 5.622 4
65-<75 yr 38943 -1.14 ** -12.128 4
75+ yr 31164 -2.8 ** -16.872 4
Total 361250 0.03 0.22 4
Person Work Status
Non-Worker 157847 -21.83 ** -196.857 3
Part Time Worker 53665 35.71 ** 89.985 2
Full Time Worker 149738 10.3 ** 28.384 3
Total 361250 0.03 0.22 4
182
TABLE 9.4 Predicted Scenario Effects on Some Variables/Indicators: Old Model
Version Activity Type m0 m1-m0 (%) sign t-value df
Work 114791 13.15 ** 30.473 2
Business 37685 9.89 ** 11.649 3
Bring-Get 50647 -4.98 ** -7.255 4
Shop-1-store 132147 -5.58 ** -12.04 2
Shop-n-store 26508 -6.54 ** -7.476 3
Service 30773 -1.33 -1.472 2
Social 72570 -0.66 -1.52 2
Leisure 76844 1.76 ** 3.99 4
Tour 47565 0.33 0.496 4
Other 9112 -6.84 ** -5.922 2
Total 598643 1.2 2.538 2
Activity Start Time
<= 10 am 154755 6.67 ** 12.981 2
10-12 am 81568 -4.39 ** -9.219 4
12-2 pm 78170 -3.08 ** -4.892 2
2-4 pm 97964 -2.69 ** -4.742 2
4-6 pm 72187 2.1 * 2.759 3
> 6 pm 113998 3.48 ** 7.455 3
Total 598643 1.2 2.538 2
Activity Trip Pattern
Single-stop 378913 1.16 * 2.996 2
After-stop 92716 0.9 1.371 3
Before-stop 92716 0.9 1.371 3
In-between stop 34298 3.28 ** 4.549 2
Total 598643 1.2 2.538 2
Activity Location
Home Zone 180365 -1.65 -2.86 2
Home Municipality 172704 1.7 ** 4.371 4
Municipality order1 92733 2.24 * 3.354 2
Municipality order2 55633 2.84 ** 11.294 2
Municipality order3 38707 3.55 ** 4.583 2
Municipality order4 26996 2.16 1.861 3
Municipality order5 31505 5.13 ** 9.554 3
Total 598643 1.2 2.538 2
183
TABLE 9.4 (cont.)
First Tour Mode m0 m1-m0 (%) sign t-value df
Car 219422 3.43 ** 6.726 2
Slow 184496 -1.34 ** -3.972 3
Public 17397 2.68 ** 5.748 4
CarPass 49528 -0.61 -1.05 4
Unknown 787 2.2 1.078 3
Total 471629 1.11 2.565 2
Number of Activity in a Tour
1 378913 1.16 * 2.996 2
2 68348 0.29 0.429 4
3 17259 1.91 1.845 2
4 4908 3.02 ** 8.55 2
> 4 2201 7.25 ** 6.347 2
Total 471629 1.11 2.565 2
Number of Tours in a Schedule
0 77177 -4.09 ** -12.867 2
1 153575 0.26 0.81 3
2 89193 3.41 ** 8.073 2
3 29699 0.97 1.699 4
> 3 11607 -3.82 ** -3.043 4
Total 361250 0.03 0.104 3
INDICATORS
Total travel time 17379946 -3.89 -0.625 2
Travel time car driver 7940591 4.56 ** 7.313 2
Travel time public 2867975 -36.74 -0.938 2
Travel time slow 4961321 0.15 0.352 3
Travel time car passenger 1588751 0.45 0.545 4
Number of tours 471629 1.11 2.565 2
Number of trips 1070272 1.16 2.55 2
Ratio trips-tours 2.269 0.05 1.864 3
Total travel distance 11586597 4.09 ** 7.014 2
Distance car driver 8409780 4.73 ** 6.799 2
Distance car passenger 1587591 1.01 1.046 3
Distance slow 672783 1.11 * 2.457 4
Distance public 916444 5.79 ** 7.887 3
Distance car driver 8409780 4.73 ** 6.799 2
184
TABLE 9.5 Predicted Scenario Effects on Some Variables/Indicators: New Model
Version
Activity Type m0 m1-m0 (%) sign t-value df
Work 115089 12.79 ** 32.421 3
Business 35932 10.78 ** 17.52 4
Bring-Get 53238 2.6 ** 6.752 4
Shop-1-store 133379 -3.15 ** -4.387 4
Shop-n-store 25328 -3.81 ** -6.136 3
Service 30111 2.2 ** 9.94 3
Social 89070 -1.21 ** -6.987 4
Leisure 81842 -0.69 -1.12 2
Tour 49668 -3.53 ** -6.355 3
Other 9506 -3.15 * -2.143 4
Total 623163 1.89 ** 7.448 2
Activity Time of Day
<= 10 am 152278 6.87 ** 42.423 2
10-12 am 78937 -3.38 ** -9.712 4
12-2 pm 74125 -1.4 ** -3.819 4
2-4 pm 94292 -1.83 * -3.021 3
4-6 pm 76311 3.14 ** 7.582 4
> 6 pm 147220 2.95 ** 10.241 3
Total 623163 1.89 ** 7.448 2
Activity Trip-Chain Pattern
Single-stop 428113 0.94 * 4.287 2
After-stop 83041 3.31 ** 8.936 4
Before-stop 83041 3.31 ** 8.936 4
In-between stop 28969 7.82 ** 8.038 4
Total 623163 1.89 ** 7.448 2
Activity Location
Home Zone 208622 -0.4 -0.912 4
Home Municipality 176818 2.31 ** 5.825 4
Municipality order1 89258 3.19 ** 6.588 4
Municipality order2 53451 3.06 ** 7.408 3
Municipality order3 37198 3.55 ** 9.142 4
Municipality order4 25510 4.59 ** 6.679 2
Municipality order5 30073 5.06 ** 14.281 3
Total 623163 1.89 ** 7.448 2
185
TABLE 9.5 (cont.)
First Tour Mode m0 m1-m0 (%) sign t-value df
Car 225592 4.28 ** 13.694 3
Slow 207409 -1.31 ** -5.519 4
Public 18526 1.4 1.946 4
CarPass 57839 -0.78 * -2.483 4
Total 511154 1.32 ** 6.051 2
Number of Activity in a Tour
1 428113 0.94 * 4.287 2
2 62941 2.12 ** 10.25 4
3 14011 5.8 ** 5.137 4
4 4074 9.56 ** 8.777 3
> 4 2014 10.66 ** 4.806 2
Total 511154 1.32 ** 6.051 2
Number of Tours in a Schedule
0 64541 -6.46 ** -28.859 3
1 156377 1.24 ** 7.4 2
2 90293 2.12 ** 5.146 3
3 33563 1.53 ** 3.333 4
> 3 16475 -0.5 -0.832 4
Total 361250 0.03 0.22 4
INDICATORS
Total travel time 18044438 -3.38 -0.526 2
Travel time car driver 7820339 5.08 ** 21.663 4
Travel time public 2917420 -36.33 -0.944 2
Travel time slow 5320978 0.94 2.71 2
Travel time car passenger 1922107 0.2 0.387 3
Number of tours 511154 1.32 ** 8.975 2
Number of trips 1134317 1.63 ** 11.374 2
Ratio trips-tours 2.219 0.31 ** 7.856 3
Total travel distance 11872101 4 ** 12.347 4
Distance car driver 8203993 4.93 ** 17.335 4
Distance car passenger 2036742 0.36 0.536 3
Distance slow 708375 2.03 ** 4.718 3
Distance public 922991 5.2 ** 6.485 2
186
As for the presence and age of children, household composition changes only slightly.
It indicates that households with children aged under 12 years decreases with 1.58%
(6-11 years) and 3.54% (<6 years). As opposed to that, households with children over
12 years of age do not change significantly and no-child households show a small
increase of 0.74%. These results indicate that with the increasing labor participation of
women, the tendency of having children decreases, and hence, no-child households
increases.
In terms of car ownership, the prediction concludes that the possession of 2 or more
cars increase with 6.27%, whereas the number of households with no cars decreases
about 5.26%. In connection with person age, there are no noticeable changes as we
would expect since this variable is constrained by zonal population data in the
synthesis. In terms of work status of the person, as expected, the number of non-
workers among household heads decreases strongly (21.83%). On the other hand, the
number of part time workers increases significantly with 35.71%. Also the number of
full-time workers increases, all be it less substantial, with 10.3%. This reflects the fact
that in the baseline a relatively large proportion of women workers are part time
workers and this is maintained in the scenario.
Table 9.4 and Table 9.5 illustrate the comparison between the baseline and labor
scenario for the old version and new version respectively. Concerning the activity-level facets, both versions predict considerable shifts in frequency distributions as
consequences of the scenario. However, we are interested here in the differences in
prediction made by the old version (Table 9.4) and the new version (Table 9.5). In
predicting the number of work activities, both versions predict similar effects as we
would expect. However, in terms of household tasks activities (bring-get, service,
shop-1-store, and shop-n-store), the predictions of both versions are quite different.
The old version predicts a decrease of activities for all household task activities. The
new version predicts a slight increase in bring-get and service activities of 2.6% and
2.2% and smaller decreases of the other household activities compared to the old
version. The explanation might be that by making explicit allocation decisions
considering both schedules of the spouses, the new model might be better able to find a
time slot in either one of the two schedules for including a task activity. Since the old
version does not consider schedules of the spouses in combination it may fail to find a
time slot in the schedule of the person that is primary responsible for the task and omit
rather than re-allocate the activity. On the other hand, for non-task discretionary
activities, social, leisure and touring, the two model versions also predict rather
different effects. Note that non-task discretionary activities are relatively often
performed jointly in the baseline. When labor participation of women increases
according to the scenario, the models predict opposite effects. The old version predicts
187
no change or an increase depending on the specific type of discretionary activity. The
new version in contrast predicts a decrease at least for the social and touring activity
(the decrease of the leisure activity is not significant). Also this difference can probably
be attributed to a specific strength of the new model. With increasing work time of the
female, there will be fewer opportunities to find a time slot where the activities can be
conducted jointly. Given a preference to conduct them jointly, a decrease in
opportunities will lead to a decrease in these activities. This effect is predicted by the
new model. The old model treating activities independently does not impose the
requirement of finding a common time slot of (a subset of) the activities across the
schedules and, therefore, finds in more cases opportunities to schedule the activities.
In terms of time of day, there are no significant differences in predictions between the
old version and the new version. Both models predict an increase of activities with a
start time before 10 am of around 6-7%. This is an expected effect of an increase in
work activities, given that work activities tend to start at early time moments of a day.
In terms of trip-chaining, the new version predicts a stronger increase of activities on
an in-between stop (7.82% versus 3.48%). This result is consistent with the prediction
of the new model that more household-task activities are maintained in the scenario
and a tendency that these activities are combined with work activities. For example,
females tend to make multiple stops from home to work and stop by at school.
Regarding locations of activities, both versions again show similar results. The number
of activities conducted in the same postcode area where the person lives decreases as a
consequence of the scenario, whereas the choice of destinations outside the own
municipality slightly increases. Thus, the prediction points out that people tend to
travel longer from home when labor participation of women increases.
Regarding transport mode choice for tours, the two models predict more or less the
same effects. There are only slight differences which may not be significant. The new
version predicts a slightly stronger increase in car driver mode (4.28% versus 3.43%),
whereas the old version predicts a slightly stronger increase in public transport mode
than the new version do (2.68% versus 1.4%). These predictions are plausible, given
the increase in income, car possession, work activities and distance to destinations.
Furthermore, both models predict an increase of tours where multiple activities are
combined (more than 4). The new version predicts a slightly stronger increase of such
complex tours, which is consistent with the earlier finding that this model predicts a
stronger increase of activities conducted on in-between stops.
In terms of the prediction of indicator variables, the old and new versions give similar
results but at the same time display some notable differences. Total travel time
188
decreases about 3 - 4%. Percentage-wise, the models predict a small increase in travel
time for car driver (5.08% and 4.56%) and a strong decrease of travel time by public
transport (36 - 37%). The models predict different effects regarding the number of trips,
number of tours and ratio trips-tours. The old version predicts that there are no effects
on these variables. In contrast, the new version predicts a small but significant increase
in each of these variables. This difference reflects the differences that we saw in terms
of number of activities, trip-chaining and number of activities per tour. Hence, the
specific sensitivity of the new model is visible even at the level of aggregate mobility
indicators. As for distance traveled across all modes, the two models both predict an
increase of around 4 %. Based on the increase of work activities alone, one may have
expected a stronger increase in mobility. We should realize, however, that the increase
takes place primarily in the part time worker segment which is characterized by
relatively short home-work distances. Furthermore, it should be noted that non-work
activities decrease in this scenario. Distance traveled by public transport increases 5 -
6%, and travel distance by car driver increases 4 - 5%. According to those predictions,
there is a tendency of people traveling longer distances by car and public transport.
Finally, the model predicts different mobility effects for the weekend days and
weekdays (not shown) that can be interpreted too as a result of an increased sensitivity
of the new model.
9.4 CONCLUSIONS AND DISCUSSION
This chapter considered the validity and sensitivity of the full ALBATROSS model by
comparing the performance of the old version and the new version. A validity test on
the basis of the MON data set established that the new version is able to predict
choice-facet frequency distributions and mobility indicators observed in the MON data
as accurately as the old version. The goodness-of-fit of the new version for most
choice facets appeared to be either equivalent or slightly better than the goodness-of-fit
of the old version. The only exceptions are time of day and trip-chaining. For these
facets the old model produced better results. The bias in time-of-day predictions in the
new version is probably due to the inclusion of joint activities. Joint activities might be
more feasible to do in the evening compared to independent activities, as a
consequence of coupling constraints, resulting in a fairly larger shift towards evening
hours. In relation to trip-chaining, the new model underpredicted the in-between stops.
This can be understood in terms of the increased difficulty of finding a feasible in-
between time slot due to additional constraints that joint activities bring along.
With regard to performance indicators, the new version of ALBATROSS shows better
predictions than the old version, for all transport modes, except car driver, in terms of
189
travel time for each mode. The prediction of the number of tours and number of trips by
the new model are also more accurate than the old model. In terms of distance traveled
by each transport mode, the new model’s predictions are better for every transport
mode except slow modes. Only in terms of total distance, the prediction of the old
version is slightly better than the new version.
In terms of a test of sensitivity, the new model proved to be more sensitive to the
impacts on situational and decision dimensions of activities, such as activity type, start
time, trip-chaining, location, etc. The scenario involved an increase in work activity
load in women’s schedules and the new model predicted somewhat different responses
that could be interpreted in terms of better representing opportunities and requirements
related to task allocation and joint activity participation. In sum, by considering
decisions of household heads on these dimensions in interaction, the system is able to
predict with increased sensitivity processes of activity re-scheduling in response to a
change. The results showed that this can lead to differences in prediction of activity
generation and travel choices that have an impact on aggregate mobility indicators (e.g.,
number of trips, shift in timing and transport mode) that are relevant for planning and
policy making. Other scenarios could be considered as well, but this case served our
purpose to evaluate the working of new model.
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Chapter 10
CONCLUSIONS AND DISCUSSION
Household members traditionally share household resources and responsibilities. Since
demand for travel is derived from the necessity of individuals to perform out-of-home
activities, in particular in multi-person households, the activities of one person may
contribute to satisfying household needs and may therefore imply that other household
members do not need to make additional trips. The vast majority of studies and models
in activity-based analysis have been based on individual decision making. However, it
is more realistic to model activity-travel patterns from the perspective of a household to
capture the interrelationships and interdependencies of the activity-travel patterns of
household members. A focus on the household as the decision-making unit is relevant
for at least three decision problems: (i) the problem of allocating limited resources to a
household member; (ii) the problem of task allocation and (iii) the problem of joint
activity participation. A good example of the resource allocation problem is a
household with fewer cars than driver licenses. In that case, the household members
need to decide who will use the car to conduct a particular activity. It automatically
means that other household members cannot use the car at the same time. An example
of task allocation is a husband dropping off a child at school, while his spouse may be
responsible for grocery shopping. Both child care and grocery shopping satisfy
household needs, but only one household member needs to conduct these activities.
191
Joint activity participation requires synchronization of activity-travel patterns in time
and space. An example is a husband and wife intending to go for lunch together during
their work time who then need to decide on the timing, duration and location of the
activity that are suitable for both of them. Therefore, the activity-travel patterns of
persons in the same household are strongly interconnected.
This thesis argued that there is a need for incorporating household decision making in
urban travel demand modeling. The daily activity-travel patterns of individuals often
reflect interactions with other household members, in particular in the form of joint
activity participation, and task and resource allocation. Explicit representation of
household decision making in comprehensive activity-based models is a research
frontier. Although some models focusing on a limited number of aspects have been
suggested, most operational comprehensive models are based on individuals.
Considering that the household is the basic unit of decision making, modeling of
household behavior is important for researchers and policy makers alike. Logically,
representing intra-household interactions is motivated by the need to bring more
consistency in addressing the interdependencies that characterize activity-travel
behavior.
The aim of this thesis is to include aspects of household decision making in a more
rigorous manner in the ALBATROSS model. The original model already incorporated
aspects of household decision-making, albeit in an implicit way. The activity-travel
patterns of household adults were sequentially developed, incorporating the schedule of
the spouse. In this thesis, a more integral approach to household decision making was
systematically incorporated in the system. Using decision tree induction, as in the
original model, a series of decision tables were derived from the MON data. The model
considers all choice facets in generating the daily activity-travel patterns at the
individual level and household level. Facets that are analyzed at the individual level are
activity type selection, activity (tasks) allocation, start times, durations, trip-chaining,
locations, and transport modes. The household level considers joint activity selection,
car allocation to work tour, start times, durations, locations, and car allocation to non-
work tours. Note that all household level facets (except car allocation to work tour)
apply to non-work activities.
The household perspective requires some modification of the structure of the
scheduling process, underlying ALBATROSS. This new, household level version of
the model was compared to the old version by assessing the goodness-of-fit and the
sensitivity of the models. This comparison test was based on the complete generated
activity schedules. A full activity-travel pattern is generated on a continuous time scale
for each simulated individual, and hence, detailed information about trips, activities
192
and socio-demographic characteristics can be derived. To determine whether the new
model predicts choice-facet frequency distributions and mobility indicators as accurate
as the earlier version, a test of validity using the MON data set was conducted. The
results demonstrate that, overall, the predictions of the new version model are
equivalent or slightly better than the old model for most aspects. Only for some
attributes, the old model outperformed the new model.
The benefits of a comprehensiveness transport demand model, such as ALBATROSS,
are however not restricted to prediction. It also means an increase in range and detail in
information that can be generated and presented to the user for assessing the impact of
scenarios. Furthermore, the systematic and better handling of household decision
making implies that the model was expected to be more sensitive to scenarios that
likely impact the household decision making. To test this, a reasonable scenario on the
basis of the synthetic population was applied involving an increase of 41 % in labor
participation of women household heads (labor-scenario). The new model proved to be
more sensitive to the impacts on situational and decision dimensions of activities.
Specifically, the new model predicted different responses that could be interpreted in
terms of a better representation of opportunities and requirements related to task
allocation and joint activity participation. By considering decisions of household heads
on these dimensions, the system is able to predict with increased sensitivity
rescheduling processes of households in response to a change. In that sense, the
ALBATROSS system has become a more powerful operational system to predict and
assess urban travel demand due to an improved inclusion of household decision making
in the system.
The current version of ALBATROSS represents an operational model that can be
applied to asses the impacts of several policies. Furthermore, although ALBATROSS
is a comprehensive model and unique in its class, there are some limitations that have
not been implemented yet. Therefore, it is recommended that the following topics are
further developed and/or examined in future research:
1. adding the travel decision The decision to travel is closely related to the activity participation decision. The
decision of conducting activity independently or jointly was examined in this study.
In fact, the travel decision could be explicitly modeled as well. In doing so, a better
connection between activity and travel decisions may be established.
2. adding ride sharing decision The current version adds a new dimension in the system, i.e. car allocation decision.
We examined it in the context of work tours and non-work tours. It is also useful to
193
model the ride sharing decision between household heads that use their own car.
Although we model car driver and car passenger, this decision is not linked to
specific persons.
3. elaborate within-household interactions by adding the presence of other
household member (if any) Currently we are only concerned with the interaction between two household heads
in performing out-of-home activities. However, to some extent, the existence of
other household members, such as children, should also be taken into consideration.
Hence, joint participation in activity and travel may be more precise and more
sensitive by including other household members.
4. adding time allocation for spouses
Spouses could coordinate their work times, especially if they have small children.
For example, there may be evidence of turn-taking behavior or synchronization of
their work times. Such behavior has not yet been implemented in ALBATROSS.
5. incorporating in-home activities
The current Dutch National data (MON) includes information about out-of-home
activities only. In order extent within-household interactions, complementary
survey data on in-home activities allows an extension of the model. In the present
case, trip data was converted to activity data so that the in-home stay periods are
known as well. The only thing missing is the differentiation of the stay-at-home
duration by activity type (what type of activities is performed at home). Hence, the
in-home and out-of-home activities could be addressed explicitly.
Time and data limitations did not allow incorporating these aspects into the model.
However, those features could be implemented in future research to further enhance the
sensitivity of the model.
194
SUMMARY
Although the importance of households as a decision making unit has been recognized
in seminal work in activity-based analysis of transport demand, most comprehensive
models have relied on individual activity-travel patterns. The transformation of these
models to household level models and the explicit consideration of resource allocation,
task allocation and joint activity participation decisions is thus a challenge and research
frontier in this field of study. To contribute to this expanding field, the aim of this PhD
study is to develop such an activity-based model. More specifically, the slightly ad hoc
treatment of household decisions in the ALBATROSS model is replaced with a
systematic incorporation of household decisions. The new variant, based on the MON
2004 data, is compared in terms of goodness-of-fit and sensitivity with the previous
version of the model.
To this end, the thesis is organized as follows. Chapter 2 provides a review of past
research efforts concerning the determinants of household decision making. We discuss
how household decision making has been treated in comprehensive activity-based
models of transport demand. This line of research started with analytical studies on
household decision making taking into account car allocation and usage decisions.
Further literatures addressed task and time allocation decisions. They found that
household types, defined by the number of household heads and work status, strongly
influence activity time allocation and trip chaining. The presence of children in the
household has a positive effect on the duration of all out-of-home activities in
household trip chaining, except for the duration of out-of-home discretionary activities
of households having children under 5 years old. This suggests that the presence of
children induces more chaining of trips and more time allocated to these trip chains.
Households having more children of 16 years of age and over are more likely to spend
time in trip chaining for out-of-home subsistence activities. Finally, they found that
flexible work arrangements tend to be correlated with less trip chaining for the work
trip.
In addition to these studies, there is also a literature on joint activity participation.
Several studies have examined the effect of household attributes on joint activity-travel
behavior. They found that joint activities involving household heads are significantly
affected by the presence of children. Couples without children living at home are more
likely to pursue joint out-of-home non-work activities than couples with children. In
households with children, most joint activities between adults are at home. In addition,
the employment status of the household heads influences whether a joint activity
originates from home or from an out-of-home contact point. In additional to analytical
studies, existing comprehensive activity-based models are reviewed in this chapter in
195
terms of their inclusion and treatment of household decisions. Comprehensive in this
context means that the model allows predicting a combination of choice facets, at least
compatible with those underlying traditional four-step models: i.e. activity generation,
destination and transport mode choice. The discussion is restricted to fully operational
models. Over the years, many activity-based models have been suggested in the
literature, including constraints-based models, micro-simulation models, (nested logit)
utility-maximizing models, suites of advanced statistical models and rule-based models.
Most of these models either do not incorporate household decisions at all, or only in a
limited way.
Chapter 3 discusses the conceptual framework of this thesis for modeling household
activity-travel behavior. Because the thesis is an attempt of elaborating the
ALBATROSS model, we discuss this model in more detail, including its conceptual
framework. Further, we explain the entire process underlying the ALBATROSS
system and the inclusion of household decision making in the process, such as joint
participation, activity allocation, car allocation for non-work tours, and some other
choice facets. Household decision making is mostly applicable to non-work activities,
but the problem of car allocation is highly relevant for work tours in car-deficient
households. Further, we summarize the methodology that was used in this study:
decision tree induction using a CHAID-based induction algorithm being the core
method of ALBATROSS.
The remaining chapters then present the results of the various derived decision tress for
the sequential choice facets that together make up the ALBATROSS model. Chapter 4 describes the results for car allocation choice focusing on work activities. In this
analysis work-tours as opposed to work trips are considered. The car allocation model
focuses on car-deficient households (i.e., more drivers than cars present) and a joint
decision between the two heads (mostly, a female and male). We also assume that both
male-female are drivers and at least one of them has a work activity on the day
considered. Furthermore, the model includes the option that none of the household
heads uses the car, but some other means of transport. The results show that the
propensity of men driving a car to the work place is higher than that of women,
particularly, when women have no work activity or women’s work place is in the same
zone as the home location. This finding is consistent with the common notion that
women use a slow or public transport mode more often to travel to activity locations.
Women tend to use the car when men have no work activities or men work at home.
Chapter 5 reports the empirical derivation of a household decision model of activity
choice taking into account joint participation and task allocation between household
heads. These are considered household-level decisions given that they involve
196
commitments of multiple persons, in particular the two-head households. Of the 10
activity categories concerned, 7 activity categories (non-work activities) are used in
this study, i.e. bring/get, shopping to 1 store, shopping to multiple store, service-related,
social, leisure, and touring. The first four activities are deemed task allocation activities
and the rest are non-task activities (discretionary). Hence, two decision trees were
derived from diary data. The activity participation model, given the large number of
observations that could be derived from the data, included more than 300 condition-
action rules. The household task allocation model also involved an extensive set of
decision rules, involving more than 90 condition-action rules. In both cases, the
validity of the decision tree is satisfactory in the sense that the derived rules are readily
interpretable and the overall goodness-of-fit of the model on a validation set is
acceptable as well.
Chapter 6 focuses on the joint participation of male-female heads in non-work
activities and attempts to model the timing and duration decisions for these activities,
using decision tree induction. Decision tree results indicated that there were 17 and 31
condition-action rules derived for the duration model and start time model, respectively.
The improvement in S-value (a measure of prediction accuracy) relative to a null
model as well as an F-statistic indicates that there is a moderately strong association
between condition variables at household, individual, activity and schedule level, on
the one hand, and the decision, on the other. The S-value shows a more substantial
improvement in the start-time model compared to the duration model. The results show
that activity type has the most significant influence in both models. In addition, time
availability for non-work activities during morning off-peak periods has a strong
influence on start time decisions. The results also suggest that there is a substantial
influence of duration decisions on start time decisions. Joint participation of household
members in activities tends to lead to longer activity duration and earlier start times.
Overall, modeling timing and duration of joint activity participation decisions at the
household level proves to have some clear advantages.
Chapter 7 discusses the development of the household location choice model taking
into account the independent and joint activities, in particular non-work activities. In
ALBATROSS, location choice is modeled for independent and joint activity
participation of the household heads based on the concept of detour time. The detour
time of a candidate location for an activity is defined as the extra travel time required
to implement the activity in the context of the current activity schedule. There were
two decision tree models for both independent and joint activity categories. The first
model relates to the decision whether or not the activity is performed at the same
location as the previous activity, whether the activity is done at the same location as the
next activity, or whether it is conducted elsewhere. The second model relates to the last
197
choice option in the first model and comprises 25 choice alternatives. It verifies the
location in terms of a combination of size - distance classes. The size class depends on
a particular activity type and the size of available facilities at the activity location. Size
is classified into 5 categories based on employment in the relevant sector for the
activity considered and distance is classified in terms of a detour travel time (by car)
also into 5 categories. The tendency of conducting a particular activity at the same
location as the previous activity is higher for independent activities than for joint
activities. The same condition also applies to activities that are conducted at the same
location as the next activity. These results imply that males and females are more likely
to conduct multiple activities at one particular location independently than jointly.
These results do make sense, since the activity-travel behavior of one person is
different from the other person, even though male-female couples live in the same
household.
Chapter 8 is concerned with car allocation behavior for non-work activities. In this
study, the assumption is similar to the assumption in Chapter 4 where tours are taken
into account instead of trips. Travel for any activity episode or set of chained activity
episodes that does not include a work activity is considered a non-work tour. The
problem of modeling this allocation problem for non-work tours is more complex than
for work tours because the decision at this stage depends considerably on the outcome
of the previous stages in the scheduling process. Hence, the car can be allocated to
male, female or none. Further, only overlapping non-work activities of the male’s and
female that occur in the same time slot are taken into account. Overlapping tours are
defined as a pair of tours conducted by respectively male and female of which the start
and/or end times of each tour (simulating use of a car for the tour) defines a fully or
partially overlapping episode. As a tour consists of a sequence of trips that starts and
ends at a particular location (i.e., home), the primary activity in each tour needs to be
determined. In order to identify the primary activity in a particular tour, we consider a
hierarchical order of activity priority. In particular, 10 activity categories are
considered in order of priority starting from work, business and other (mandatory)
activities. A group of non-work activities is considered, such as escorting, shopping
(daily and non-daily), service-related, social, leisure, and touring. Since business and
other mandatory activities are not considered primary work activities, they are not dealt
with in the first stage of the scheduling process and, hence, they are also considered as
non-work activities in this model.
The results show a satisfactory improvement in goodness-of-fit of the decision tree
model compared to the null model. Gender seems to play an important role. A
descriptive analysis indicates that men more often than women get the car for non-
work tours for which a car allocation decision needs to be made. Tour-level attributes
198
are shown to influence the household car allocation decision for non-work tours. The
decision to allocate the car is considerably influenced by the longest distance (travel
time) from home to a particular location in a tour of men and women. The probability
that the men and women get the car monotonically increases with increasing travel
time. Socio-economic and situational factors have less influence on the car allocation
decision. Overall, men have more influence on the car allocation decision for non-work
tours, as indicated by the number of influential variables that relates to the males in the
impact table.
Chapter 9 discusses the results of the integrated model of ALBATROSS. In order to
test the performance of the ALBATROSS system based on all decision tables for the
assumed scheduling process, the validity and sensitivity of the integrated model were
evaluated and compared with the performance of the old model. First, the validity of
the model versions was compared by evaluating the extent to which the model is able
to reproduce observed frequency distributions and mobility indicators in the MON
dataset. In that sense, no major differences were expected. Instead, it was expected that
the new model is able to reproduce the aggregated distributions as well as the existing
model. A Second effort was to examine the sensitivity of the models by applying the
models to a particular scenario of change in the Dutch population. It was expected that
the new model was more sensitive to such scenarios. The scenario assumed an increase
of 41 % in labor participation of women household heads (labor scenario) assuming
the year 2000 as the base year. A fraction of 10% of the Dutch population in the year
2000 was generated using the synthesis module of ALBATROSS for the baseline and
the labor scenario. As expected, in the context of validity test, the new model showed
equal or slightly better goodness-of-fit for most choice facets, except for time of day
and trip-chaining. The new model proved to be more sensitive to facets such as activity
type, start time, trip-chaining, location, etc., in response to scenarios change. In
particular, the new model predicted somewhat different responses that could be
interpreted in terms of the better representation of opportunities and requirements
related to task allocation and joint activity participation. In sum, by considering
decisions of household heads in interaction, the system is able to predict with
increased sensitivity activity-travel rescheduling processes of households in response
to change.
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APPENDIX:
DECISION TREES
TABLE AI-1.1 Car Allocation to Work Tour
TTbcF 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 2 2 2
TTcM 0 0 1 1 1 2 2 2 2 3-4 3-4 3-4 3-4 - - - - - - -
Day 0,3 1,4,2,5,6 - - - - - - - - - - - - - - - - - -
TTbcM - - 0-2 0-2 3-4 0-1 2-4 2-4 2-4 0-3 4 - - 0 0 1 2-4 - - -
DurM - - 0-2 3-4 - - - - - - - - - - - - - - - -
SEC - - - - - - 0-1 2-3 2-3 - - - - 0,1,2 3 - - - - -
Child - - - - - - 0 1,3,2 - - - - - - - - - - -
PParkM - - - - - - - - - 0-1 0-1 2-4 2-4 - - - - - - -
TTptM - - - - - - - - - - - 0-1 2-4 - - - - 0 0 1-4
TTcF - - - - - - - - - - - - - - - - - 0-1 2-4 -
TrAcF - - - - - - - - - - - - - - - - - - - 0
Male 0.075 0.267 0.539 0.351 0.315 0.735 0.614 0.547 0.32 0.728 0.536 0.291 0.535 0 0 0.613 0.262 0 0 0.442
Female 0 0 0 0 0 0 0 0 0 0 0 0 0 0.78 0.574 0.333 0.584 0.22 0.465 0.299
None 0.925 0.733 0.461 0.649 0.685 0.265 0.386 0.453 0.68 0.272 0.464 0.709 0.465 0.22 0.426 0.053 0.154 0.78 0.535 0.259
N 93 146 128 94 184 83 88 75 103 419 110 55 86 109 54 75 149 59 86 147
R1 R2 R3 R4 R5 R6 R7 R8 R9 R10 R11 R12 R13 R14 R15 R16 R17 R18 R19 R20
200
TABLE AI-1.2 Car Allocation to Work Tour
TTbcF 2 3 3 3 3 3 3 4 4
TTbcM - 0 0 1 2-4 2-4 2-4 - -
TTptM 1-4 - - - - - - - -
TTcF - 0-1 2-4 - 0-1 0-1 2-4 - -
TrAcF 1 - - - - - - - -
TTcM - - - - 0-1 2-4 - - -
Mwork - - - - - - - 0 1
Male 0.296 0 0 0.712 0.272 0.547 0.378 0 0.474
Female 0.574 0.203 0.43 0.106 0.141 0.156 0.378 0.5 0.308
None 0.13 0.797 0.57 0.182 0.587 0.297 0.243 0.5 0.218
N 54 177 79 66 92 64 111 50 78
R21 R22 R23 R24 R25 R26 R27 R28 R29
201
TABLE AII-1.1 Household Activity Participation
HHact 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
nbr 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 2
Child 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ModeF 0,3,4 0,3,4 0,3,4 0,3,4 0,3,4 0,3,4 0,3,4 0,3,4 0,3,4 0,3,4 0,3,4 0,3,4 0,3,4 0,3,4 0,3,4 0,3,4 1 2 - -
ModeHH - - 0,1,3,4 0,1,3,4 0,1,3,4 0,1,3,4 0,1,3,4 0,1,3,4 0,1,3,4 0,1,3,4 0,1,3,4 0,1,3,4 0,1,3,4 0,1,3,4 0,1,3,4 2 - - - -
Dist1 - - 0-2 0-2 0-2 0-2 0-2 0-2 3 4-5 - - - - - - - - - -
SEC - - 0-2 0-2 0-2 0-2 0-2 0-2 0-2 0-2 3 3 3 3 - - - - - -
AgeF 0-2 3-4 - - 0-2 0-2 3-4 - - - - - - - - - - - - -
DrivF - - 0 1 1 1 1 - - - - - - - - - - - -
Ncar 0 0 1-2 1-2 1-2 1-2 1-2 1-2 1-2 1-2 1-2 1-2 1-2 1-2 1-2 1-2 - - - -
yOthM - - 0 0 0 0 0 0 0 0 0 0 0 0 1 - - - - -
Dist3 - - - - 0-1 2-5 - - - - - - - - - - - - - -
Time2F - - - - - - - - - - 0-1 0-1 2-4 2-4 - - - - - -
WstatF - - - - 0 0 0 1-2 - - - - - - - - - - - -
yWorkF - - - - - - - - - - - - 0 1 - - - - - -
Time5F - - 0-2 3-4 3-4 3-4 3-4 3-4 - - - - - - - - - - - -
Time1C - - - - - - - - - - 0 1-4 - - - - - - - -
Yes 0.022 0 0.092 0.019 0.108 0.042 0.02 0.017 0.071 0.035 0.063 0.011 0.06 0.102 0.118 0.153 0.101 0.268 0.436 0.259
No 0.978 1 0.908 0.981 0.892 0.958 0.98 0.983 0.929 0.965 0.937 0.989 0.94 0.898 0.882 0.847 0.899 0.732 0.564 0.741
N 274 402 98 483 194 284 342 354 393 2720 207 370 1979 147 119 85 159 138 427 205
R1 R2 R3 R4 R5 R6 R7 R8 R9 R10 R11 R12 R13 R14 R15 R16 R17 R18 R19 R20
202
TABLE AII-1.2 Household Activity Participation
HHact 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
Day - 0,2,3,4 0,2,3,4 0,2,3,4 0,2,3,4 0,2,3,4 0,2,3,4 1 1 5,6 - - - - 0,1,3 2,5,6,4 0,5,6 1,4,2 1,4,2
nbr 3-5 0 0 0 0 0 0 0 0 0 1 1 1 1 1 2 2 2 2 2
Child 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
DurHH - - - - - - - - - - 0-3 4 - - - - - - - -
ModeF - - - - - - - - - - 0,4,1,2 0,4,1,2 3 - - - - - - -
NworkHH - - - - 0 1-4 - - - - - - - - - - - - -
AgeF - 0 0 0 0 1-4 1-4 - - - - - - - - - - - - -
AgeM - - - 0 1-4 - - 0 1-4 - - - - - - - - - - -
Urban - - - - - - - - - - 0-3 0-3 0-3 4 4 - - - - -
Time1F - 0-2 3-4 3-4 3-4 - - - - - - - - - - - - - - -
Comp - - 2 3,4 3,4 - - - - - - - - 2 3,4 2,4 2,4 3 3 3
Time3F - - - - - - - - - - - - - - - - - - 0-1 2-4
Yes 0.5 0.556 0.437 0.246 0.37 0.476 0.656 0.42 0.638 0.061 0.855 0.961 0.765 0.819 0.723 0.683 0.407 0.412 0.184 0.356
No 0.5 0.444 0.563 0.754 0.63 0.524 0.344 0.58 0.362 0.939 0.145 0.039 0.235 0.181 0.277 0.317 0.593 0.588 0.816 0.644
N 90 180 279 211 146 105 445 119 232 512 186 102 81 215 285 189 108 85 87 146
R21 R22 R23 R24 R25 R26 R27 R28 R29 R30 R31 R32 R33 R34 R35 R36 R37 R38 R39 R40
203
TABLE AII-1.3 Household Activity Participation
HHact 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
Day 3 0,5,6,1,3 0,5,6,1,3 0,5,6,1,3 2,4 - - - 0,4,1,2,3 0,4,1,2,3 0,4,1,2,3 0,4,1,2,3 0,4,1,2,3 0,4,1,2,3 0,4,1,2,3 5 6 - - -
nbr 2 3 3 3 3 4 4 5 0 0 0 0 0 0 0 0 0 1 1 2
Child 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2
SEC - - - - - - - - - - - 0-2 3 - - - - - - -
DurF - - - - - - - - - - - - - 0-1 2-4 - - 0-1 2-4 -
NworkHH - 0-1 0-1 2-4 - - - - - - - - - - - - - - - -
AgeF - 0 1-4 - - - - - - - - - - - - - - - -
Dist2 - - - - - - - - 0-2 0-2 3-4 3-4 3-4 5 5
Comp 3 - - - - - - - - - 2,4 3 3 - - - - - - -
Time3C - - - - - 0-1 2-4 - - - - - - - - - - - - -
nEmp3 - - - - - - - - 0-3 4-5 - - - - - - - - - -
Yes 0.6 0.885 0.711 0.627 0.487 0.133 0.331 0.456 0.271 0.495 0.595 0.348 0.547 0.421 0.255 0.211 0.118 0.729 0.601 0.25
No 0.4 0.115 0.289 0.373 0.513 0.867 0.669 0.544 0.729 0.505 0.405 0.652 0.453 0.579 0.745 0.789 0.882 0.271 0.399 0.75
N 85 78 97 75 78 90 124 114 166 109 131 89 95 356 102 152 178 373 153 12.8
R41 R42 R43 R44 R45 R46 R47 R48 R49 R50 R51 R52 R53 R54 R55 R56 R57 R58 R59 R60
204
TABLE AII-1.4 Household Activity Participation
HHact 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2
Day - - 0,6,2,1,3,4 0,6,2,1,3,4 0,6,2,1,3,4 0,6,2,1,3,4 5 - - 0,1 0,1 0,1 0,1 0,1 0,1 0,1 0,1 0,1 0,1 0,1
nbr 3 4-5 0 0 0 0 0 1 2-5 - - - - - - - - - - -
Child 2 2 3 3 3 3 3 3 3 - - - - - - - - - - 0
nsh1 - - - - - - - - - 0-1 0-1 0-1 0-1 0-1 0-1 2 3-5 0 0 1-5
DurHH - - - - - - - - - 0-1 0-1 0-1 0-1 0-1 0-1 0-1 0-1 2-3 2-3 2-3
Dist1 - - - 0-3 0-3 4-5 - - - 0 1-5 - - - - - - - - -
DurF - - - - - - - - - - - - - - - - - 0-2 3-4 -
DrivF - - 0 1 1 1 - - - - - - - - - - - - - -
Dist2 - - - 0-1 2-5 - - - - - - - - - - - - - - -
DrivM - - - - - - - - - - - - - 0 1 - - - - -
Dist3 - - - - - - - - - 0 0 1-4 1-4 5 5 - - - - -
WstatF - - - - - - - - - - - 0-2 1 - - - - - - -
Yes 0.601 0.25 0.011 0.108 0.035 0.136 0.218 0.481 0.327 0.283 0.535 0.542 0.471 0.351 0.471 0.308 0.451 0.485 0.265 0.255
No 0.399 0.75 0.989 0.892 0.965 0.864 0.782 0.519 0.673 0.717 0.465 0.458 0.579 0.649 0.529 0.692 0.549 0.515 0.744 0.745
N 153 128 95 213 200 610 170 135 98 226 127 1106 126 114 1173 409 237 709 82 141
R61 R62 R63 R64 R65 R66 R67 R68 R69 R70 R71 R72 R73 R74 R75 R76 R77 R78 R79 R80
205
TABLE AII-1.5 Household Activity Participation
HHact 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
Day 0,1 0,1 0,1 0,1 0,1 0,1 0,1 0,1 0,1 0,1 0,1 0,1 0,1 0,1 0,1 2,3 2,3 2,3 2,3 2,3
Child 1,3,2 - - - - - - - - - - - - - - - - - - -
nsh1 1-5 1-5 - - - - 0 0 0 0 0 0 1-5 1-5 1-5 0 0 0 0 0
DurHH 2-3 2-3 4 4 4 4 4 4 4 4 4 4 4 4 4 0-2 0-2 0-2 0-2 0-2
Dist1 - - - - - - - - - - - - - - - 0 0 1-4 5 5
Time4F - - 0 0 0 0 1-4 1-4 1-4 1-4 1-4 1-4 1-4 1-4 1-4 - - - - -
DrivM - - - - - - - - - - - - - - - - - - 0 1
Dist3 - - - - - - - - - - - - - - - 0 1-5 - - -
Urban - - - - - - 0-2 3-4 - - - - - - - - - - - -
Time1F - - - - - - - - 0 1-3 4 - - - - - - - - -
SizePop - - - 0-2 3-5 - - - 0-2 0-2 0-2 3-5 - - - - - - - -
Time4M - - - - - - - - - - - - 0-1 0-1 2-4 - - - - -
NworkM - - 0 1 1 2 - - - - - - - - - - - - - -
Comp 2 3,4 - - - - - - - - - - - - - - - - - -
Time4C - - - - - - - - - - - - - - - - - - - 0-1
Time5C - - - - - - 0-1 0-1 2-4 2-4 2-4 2-4 - - - - - - - -
yWorkF - - - - - - - - - - - - 0 1 - - - - - -
Yes 0.113 0.328 0.383 0.117 0.218 0.305 0.64 0.458 0.398 0.234 0.433 0.508 0.201 0.107 0.301 0.372 0.514 0.581 0.379 0.671
No 0.887 0.672 0.617 0.883 0.782 0.695 0.36 0.542 0.602 0.766 0.567 0.492 0.799 0.893 0.699 0.628 0.486 0.419 0.621 0.329
N 106 247 107 392 147 95 86 168 93 141 252 122 214 140 103 199 257 1032 95 85
R81 R82 R83 R84 R85 R86 R87 R88 R89 R90 R91 R92 R93 R94 R95 R96 R97 R98 R99 R100
206
TABLE AII-1.6 Household Activity Participation
HHact 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
Day 2,3 2,3 2,3 2,3 2,3 2,3 2,3 2,3 2,3 2,3 2,3 2,3 2,3 2,3 2,3 2,3 2,3 2,3 2,3 2,3
nsh1 0 1 1 1 1 1 1 2 2 2 3-5 0 0 1-5 1-5 0 0 0 0 0
DurHH 0-2 0-2 0-2 0-2 0-2 0-2 0-2 0-2 0-2 0-2 0-2 3 3 3 3 4 4 4 4 4
Dist1 5 - - - - - - - - - - - - 0-3 4-5 - - - - -
SEC - - - - - - - - - - - - - - - 0-2 3 - - 0-2
Time3M - - - - - - - - - - - - - - - 0-2 0-2 3-4 - -
Time4F - - - - - - - - - - - - - - - 0 0 0 1-3 4
AgeF - - - - - - - - - - - 0 1-4 - - - - - - -
yBusiM - 0 0 1 - - - - - - - - - - - - - - - -
yWorkM - 0 0 0 1 - - - - - - - - - - - - - - -
AgeM - - - - - - - 0-3 0-3 4 - - - - - - - - - -
Ncar - - - - - 0-1 2 - - - - - - - - - - - - -
Dist2 - 0-3 4-5 - - - - 0-1 2-5 - - - - - - - - - -
DrivM 1 - - - - - - - - - - - - - - - - - - -
NworkM - - - - - - - - - - - - - - - - - - - 0-1
Comp - 2,3 2,3 2,3 2,3 4 4 - - - - - - - - - - - - -
Time4C 2-4 - - - - - - - - - - - - - - - - - - -
Yes 0.526 0.576 0.414 0.32 0.349 0.576 0.43 0.398 0.274 0.167 0.438 0.351 0.525 0.379 0.196 0.118 0.24 0.387 0.369 0.43
No 0.474 0.424 0.586 0.68 0.651 0.424 0.57 0.602 0.726 0.833 0.562 0.649 0.475 0.621 0.804 0.882 0.76 0.613 0.631 0.57
N 745 132 169 75 209 604 86 133 376 84 324 131 316 153 153 186 175 142 111 402
R101 R102 R103 R104 R105 R106 R107 R108 R109 R110 R111 R112 R113 R114 R115 R116 R117 R118 R119 R120
207
TABLE AII-1.7 Household Activity Participation
HHact 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
Day 2,3 2,3 2,3 2,3 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
nsh1 0 0 1-5 1-5 0 0 0 0 0 0 0 1 1 1 1 2 2 2 3-5 3-5
DurHH 4 4 4 4 - - - - - - - - - - - - - - - -
Dist1 - - - - - 0-1 2-5 2-5 2-5 - - - - - - - - - - -
SEC 3 - - - - - - - - - - - 0-2 0-2 3 0-1 2-3 2-3 - -
Time4F 4 4 - - - - - - - - - - - - - - - - - -
AgeF - - - - 0 1-2 1-2 1-2 1-2 3 4 - - - - - - - - -
DrivF - - - - - - - 0 1 - - - - - - - - - - -
yWorkM - - - - - - - - - - - 0 1 1 1 - - - 0 1
NworkF - - - - 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Time4M - - 0 1-4 - - - - - - - - 0 1-4 - - - - - -
NworkM 0-1 2 - - - - - - - - - - - - - - - - - -
WstatM - - - - - - - - - - - - - - - - 0 1,2 - -
Dist4 - - - - - - 0-2 3-5 3-5 - - - - - - - - - - -
Yes 0.528 0.648 0.191 0.307 0.509 0.542 0.786 0.747 0.624 0.559 0.411 0.565 0.245 0.443 0.467 0.299 0.557 0.381 0.52 0.263
No 0.472 0.352 0.809 0.693 0.491 0.458 0.214 0.253 0.376 0.441 0.589 0.435 0.755 0.557 0.533 0.701 0.443 0.619 0.48 0.737
N 288 88 456 257 220 236 98 87 545 245 90 536 106 97 152 197 97 160 252 76
R121 R122 R123 R124 R125 R126 R127 R128 R129 R130 R131 R132 R133 R134 R135 R136 R137 R138 R139 R140
208
TABLE AII-1.8 Household Activity Participation
HHact 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 3
Day 4 4 4 4 4 5 5 5 5 5 5 5 5 5 5 6 6 6 6 0
nsh1 0 0 0 1-5 0-1 0-1 0-1 0-1 2 2 2 2 3 4-5 0 0 0 1-5 0
nshn - - - - - - - - - - - - - - - - - - - 0
SEC - - - - - - 0-1 0-1 2-3 - 0-2 0-2 3 - - - - - - -
Time3M - - - - - 0 1-4 1-4 1-4 - - - - - - - - - - -
DurF 0-2 0-2 3-4 - - - - - - - - - - - - - - - - -
DrivF - - - - - - - - - 0 1 1 1 - - - - - - -
NworkF 1 1 1 1 2 - - - - - - - - - - - - - - -
Ncar - - - - - - - - - - 0-1 2 - - - - - - - -
Dist3 - - - - - - - - - - - - - - - 0 1-5 - - -
Time4M 0-3 4 - - - - - - - - - - - - - - - - - -
WstatM - - - - - - 0 1,2 - - - - - - - - - - - -
Comp - - - - - - - - - - - - - - - 2,3 2,3 4 - -
Time3F - - - - - - - - - - - - - - - - - - - 0-2
Time5F - - - - - - - - - - - - - - - - - - - 0-3
Yes 0.429 0.596 0.311 0.302 0.491 0.364 0.503 0.604 0.64 0.232 0.306 0.446 0.495 0.556 0.461 0.132 0.057 0.026 0.385 0.013
No 0.571 0.404 0.689 0.698 0.509 0.636 0.497 0.396 0.36 0.768 0.694 0.554 0.505 0.444 0.539 0.868 0.943 0.974 0.615 0.987
N 105 146 193 288 112 99 469 369 1623 95 173 92 212 232 254 167 935 501 135 233
R141 R142 R143 R144 R145 R146 R147 R148 R149 R150 R151 R152 R153 R154 R155 R156 R157 R158 R159 R160
209
TABLE AII-1.9 Household Activity Participation
HHact 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
Day 0 0 0 0 0 0 0 1,2,3 1,2,3 1,2,3 1,2,3 1,2,3 1,2,3 1,2,3 1,2,3 1,2,3 1,2,3 1,2,3 1,2,3 1,2,3
nsh1 0 0 0 0 1-5 1-5 1-5 0 0 0 0 0 0 1 1 1 1 2-5 2-5 2-5
nshn 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
DurHH - - - - - - - - - - - - - - - - - - - 0-3
ModeF - - - - - - - 0,1,2 0,1,2 0,1,2 0,1,2 0,1,2 0,1,2 0,1,2 0,1,2 0,1,2 0,1,2 0,1,2 0,1,2 0,1,2
Dist1 - - - - - - - 0 1-3 4-5 - - - - - - - - -
SEC - - - - 0-2 3 - - - - - - - - - 0-1 2-3 - - -
yWorkM - - - - - - - - - - - - - - - - - 0 0 1
Ncar - - - - 0-1 0-1 2 - - - - - - - - - - - - -
Dist3 - - - - - - - - - - - - - - - - - 0 1-5 -
nEmp2 - - - - - - - 0 1-4 1-4 1-4 1-4 5 - - - - - - -
SizePop - 0 0 1-5 - - - - - - - - - 0 1-5 - - - - -
WstatM - - - - - - - - 0 0 0 1,2 - 0,1 0,1 2 2 - - -
Time3F 0-2 3-4 3-4 3-4 - - - - - - - - - - - - - - - -
Time1M - 0-2 3-4 - - - - - - - - - - - - - - - - -
Time5F 4 - - - - - - - - - - - - - - - - - - -
Yes 0.067 0.119 0.039 0.15 0.039 0.088 0.015 0.114 0.129 0.34 0.218 0.16 0.074 0.069 0.151 0.024 0.083 0.154 0.062 0.012
No 0.933 0.881 0.961 0.85 0.961 0.912 0.985 0.886 0.871 0.66 0.782 0.84 0.926 0.931 0.849 0.976 0.917 0.846 0.938 0.988
N 89 151 204 501 413 170 273 990 170 191 147 545 216 276 403 209 472 78 612 165
R161 R162 R163 R164 R165 R166 R167 R168 R169 R170 R171 R172 R173 R174 R175 R176 R177 R178 R179 R180
210
TABLE AII-1.10 Household Activity Participation
HHact 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
Day 1,2,3 1,2,3 1,2,3 1,2,3 1,2,3 1,2,3 1,2,3 1,2,3 4 4 4 4 4 4 4 5 5 5 5 5
nsh1 2-5 - - - - - - - 0 0 0 1-5 1-5 1-5 1-5 0 0 0 1 1
nshn 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
DurHH 4 - - - - - - - - - - - - - - - - - - -
ModeF 0,1,2 3,4 3,4 3,4 3,4 3,4 3,4 3,4 - - - - - - - - - - - -
Time4F - - - 0-1 0-1 2-4 2-4 2-4 - - - - - - - - - - - -
DurF - - - - - - - - 0-2 0-2 3-4 - - - - - - - - -
AgeF - - - - - - - - - - - - - - - - - - 0 1-4
yWorkM 1 0 0 1 1 1 1 1 - - - - - - - - - - - -
Dist2 - - - - - - - - - - - - - - - 0-1 2-4 5 - -
DrivM - - - - - - - - 0 1 - - - - - - - - - -
Dist3 - - - - - 0-2 3 4-5 - - - - - - - - - - - 0-2
Urban - - - - - - - - - - - 0-2 3-4 3-4 3-4 - - - - -
nEmp2 - - - - - - - - - - - 0-3 0-3 4-5 - - - - -
SizePop - 0-2 3-5 - - - - - - - - - - - - - - - - -
nEmp1 - - - - - - - - - - - - 0 1-5 - - - - - -
Time5F - - - 0-2 3-4 - - - - - - - - - - - - - -
Yes 0.071 0.109 0 0.012 0.048 0.021 0.138 0.066 0.106 0.211 0.082 0.133 0.094 0.04 0.146 0.235 0.407 0.172 0.321 0.261
No 0.929 0.891 1 0.988 0.952 0.979 0.862 0.934 0.894 0.789 0.917 0.867 0.906 0.96 0.854 0.765 0.593 0.828 0.679 0.739
N 84 331 102 427 208 189 87 319 94 693 144 362 351 299 103 183 204 233 84 111
R181 R182 R183 R184 R185 R186 R187 R188 R189 R190 R191 R192 R193 R194 R195 R196 R197 R198 R199 R200
211
TABLE AII-1.11 Household Activity Participation
HHact 3 3 3 3 3 3 3 3 3 3 3 3 3 4 4 4 4 4 4 4
Day 5 5 5 6 0,3 1,4,2 5,6 - - - - - - 0,4,1,2,3 0,4,1,2,3 0,4,1,2,3 0,4,1,2,3 0,4,1,2,3 0,4,1,2,3 0,4,1,2,3
Child - - - - - - - - - 0,3,2 1 - - - - - - - - -
nsh1 1 2-5 2-5 - 0 0 0 1-5 - - - - - 0 0 0 0 1-5 - -
nser - - - - - - - - - - - - - 0 0 0 0 0 0 0
nshn 0 0 0 0 1 1 1 1 1 1 1 2 3 0 0 0 0 0 0 0
DurHH - - - - 0 0 0 0 1 2-4 2-4 - - 0-1 0-1 0-1 0-1 0-1 0-1 0-1
Dist1 - - - - - - - - - - - - - 0 0 0 0 0 0 1-4
AgeF 1-4 - - - - - - - - - - - - - - - - - - -
yBusiM - - - - - - - - - - - - - - - - - - - 0
Dist2 - - - - - - - - - - - - - 0-2 0-2 0-2 0-2 0-2 3-5 -
Dist3 3-5 - - - - - - - - - - - - - 0 0 1-5 - - -
Urban - - - - - - - - - - - - - - 0 1-4 - - - -
nEmp2 - - - - - - - - - - - - - 0-3 4-5 4-5 4-5 - - -
Comp - 2 3,4 - - - - - - - - - - - - - - - - -
Yes 0.109 0.147 0.075 0.019 0.566 0.391 0.711 0.369 0.348 0.165 0.049 0.153 0.35 0.146 0.015 0.086 0.139 0.178 0.215 0.231
No 0.891 0.853 0.925 0.981 0.434 0.609 0.289 0.631 0.652 0.835 0.951 0.847 0.65 0.854 0.985 0.914 0.861 0.822 0.785 0.769
N 201 156 415 1594 106 215 159 317 92 334 123 476 117 158 132 81 79 304 144 1515
R201 R202 R203 R204 R205 R206 R207 R208 R209 R210 R211 R212 R213 R214 R215 R216 R217 R218 R219 R220
212
TABLE AII-1.12 Household Activity Participation
HHact 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
Day 0,4,1,2,3 0,4,1,2,3 0,4,1,2,3 0,4,1,2,3 0,4,1,2,3 0,4,1,2,3 0,4,1,2,3 0,4,1,2,3 0,4,1,2,3 0,4,1,2,3 0,4,1,2,3 0,4,1,2,3 0,4,1,2,3 0,4,1,2,3 0,4,1,2,3 0,4,1,2,3 0,4,1,2,3 0,4,1,2,3 0,4,1,2,3 0,4,1,2,3
nsh1 - 0 0 0 1-5 1-5 - - 0 1-5 1-5 1-5 - - - - - - - -
nser 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
nshn 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
DurHH 0-1 0-1 0-1 0-1 0-1 0-1 2-3 2-3 2-3 2-3 2-3 2-3 4 4 4 4 4 4 4 4
Dist1 1-4 5 5 5 5 5 - - - - - - - - - - - - - -
SEC - - - - - - 0-1 0-1 2-3 2-3 2-3 2-3 - - - - - - - -
yBusiM 1 - 0 1 - - - - - - - - - - - - - - - 0
AgeM - - - - - - - - - 0 1-4 1-4 - - - - - - - -
Dist3 - - - - - - - - - - - - 0-4 5 - - - - - -
DurM - - - - - - - - - - - - - - - - - - - 0-1
NworkM - - - - - - - - - - 0-1 2 - - - - - - - -
Time3F - - - - - - 0-1 2-4 - - - - - - - - - - - -
nEmp1 - - - - - - - - - - - - 0-1 0-1 2 3-5 3-5 3-5 - -
Time3C - - - - - - - - - - - - 0-3 0-3 0-3 0-3 0-3 0-3 4 -
Time5C - 0-3 4 4 - - - - - - - - - - - - - - - -
WstatF - - - - 0-2 1 - - - - - - - - - - - - - -
yWorkF - - - - - - - - - - - - - - - 0 1 - - -
Time5M - - - - - - - - - - - - - - - 0-1 0-1 2-4 - -
Time1C - - - - - - - - - - - - 0-1 0-1 0-1 0-1 0-1 0-1 0-1 2-4
Yes 0.142 0.06 0.172 0.076 0.184 0.317 0.01 0.111 0.124 0.281 0.153 0.256 0.016 0.073 0.149 0.033 0 0.071 0.145 0.203
No 0.858 0.94 0.828 0.924 0.816 0.683 0.99 0.899 0.876 0.719 0.847 0.744 0.984 0.927 0.851 0.967 1 0.929 0.855 0.797
N 219 116 686 79 798 123 96 397 582 89 504 86 185 559 87 92 126 378 117 172
R221 R222 R223 R224 R225 R226 R227 R228 R229 R230 R231 R232 R233 R234 R235 R236 R237 R238 R239 R240
213
TABLE AII-1.13 Household Activity Participation
HHact 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
Day 0,4,1,2,3 0,4,1,2,3 0,4,1,2,3 0,4,1,2,3 0,4,1,2,3 5 5 5 6 6 6 - - - - - - - - -
nbr - - - - - 0 0 1-5 - - - - - - - - - - - -
nsh1 - - - - - 0 1-5 - - - - - 0 0 0 1 2-5 - - -
nser 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1
nshn 0 0 0 0 0 0 0 0 0 0 0 1-3 - - - - - - - -
DurHH 4 4 4 4 4 - - - - - - - - - - - - - - -
AgeF - - - - - - - - - - - - 0-2 3-4 - - - - - -
yBusiM 1 - - - - - - - - - - - - - - - - - - -
yOthM - - - - - - - - - - - - - - - - - - - -
Dist2 - - - - - - - - 0 1 2-5 - - - - - - - - -
ModeM - - - - - - - - - - - - 0,1 0,1 0,1 0,1 0,1 2,4,3 2,4,3 2,4,3
Dist3 - - - - - - - - - - - - 0-4 0-4 5 - - - - -
Urban - - 0-3 0-3 4 - - - - - - - - - - - - - - -
DurM 0-1 2-4 2-4 2-4 2-4 - - - - - - - - - - - - 0-3 0-3 0-3
Time1F - 0-3 4 4 4 - - - - - - - - - - - - - - -
nEmp1 - - - - - - - - - - - - - - - - - 0 1-2 3-5
WstatF - - 0,1 2 - - - - - - - - - - - - - - - -
Time1C 2-4 2-4 2-4 2-4 2-4 - - - - - - - - - - - - - - -
Yes 0.105 0.068 0.037 0.179 0.151 0.048 0.092 0.174 0.017 0.099 0.047 0 0.425 0.644 0.366 0.29 0.373 0.283 0.135 0.3
No 0.895 0.932 0.963 0.821 0.849 0.952 0.908 0.856 0.983 0.901 0.953 1 0.575 0.356 0.634 0.71 0.627 0.717 0.865 0.7
N 114 614 161 78 423 421 753 109 347 141 1058 1158 167 37 186 293 292 145 126 150
R241 R242 R243 R244 R245 R246 R247 R248 R249 R250 R251 R252 R253 R254 R255 R256 R257 R258 R259 R260
214
TABLE AII-1.14 Household Activity Participation
HHact 4 4 4 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
Day - - - 0,1,2 0,1,2 0,1,2 0,1,2 0,1,2 0,1,2 0,1,2 0,1,2 0,1,2 0,1,2 0,1,2 3,4 3,4 3,4 3,4 3,4 3,4
nser 1 2 3 - - - - - - - - - - - - - - - - -
DurHH - - - - - - - - - - - - - - 0 0 0 0 0 0
ModeHH - - - 0 0 0 0 1,3 1,3 1,3 1,3 1,3 1,3 2,4 - - - - - -
Time3M - - - - - - - 0 0 0 1-3 4 4 - - - - - - -
yBusiM - - - - - 0 1 - - - - - - - - - 0 0 0 1
DrivF - - - 0 0 1 1 - - - - - - - - - - - - -
AgeM - - - - - - - - - - - - - - 0 1-4 1-4 1-4 1-4 1-4
Ncar - - - - - - - - - - - - - - - 0 1-2 1-2 1-2 1-2
ModeM 2,4,3 - - - - - - - - - - - - - - - - - - -
Dist3 - - - 0-2 3-5 - - - - - - - - - - - 0 1-5 1-5 -
nEmp2 - - - - - - - - - - - - - - - - - 0-2 3-5 -
DurM 4 - - - - - - - - - - - - - - - - - - -
Time1F - - - - - - - 0 1-4 1-4 - - - - - - - - - -
Time4M - - - - - - - - 0-1 2-4 - - - - - - - - - -
Time5M - - - - - - - - - - - 0-2 3-4 - - - - - - -
Yes 0.105 0.198 0.417 0.016 0.065 0.085 0.017 0.038 0.007 0.036 0.054 0.039 0.005 0.031 0.271 0.034 0.051 0.111 0.164 0.02
No 0.895 0.802 0.583 0.984 0.935 0.915 0.983 0.962 0.993 0.964 0.946 0.961 0.995 0.969 0.729 0.966 0.949 0.889 0.836 0.98
N 95 494 168 307 460 1736 173 312 1212 169 129 128 662 1081 96 179 178 1067 347 100
R261 R262 R263 R264 R265 R266 R267 R268 R269 R270 R271 R272 R273 R274 R275 R276 R277 R278 R279 R280
215
TABLE AII-1.15 Household Activity Participation
HHact 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
Day 3,4 3,4 3,4 3,4 3,4 3,4 3,4 3,4 3,4 3,4 5 5 5 5 5 5 6 6 6 6
nsh1 - - - - - - - - - - 0 0 0 1 1 2-5 - - - -
DurHH 1-2 1-2 1-2 1-2 3-4 3-4 3-4 3-4 3-4 - - - - - - - - - - -
Dist1 - - - - - - - - - - - - - - - - - - 0 1-5
SEC - 0-1 2-3 - - - - - - - - - - - - - - - - -
NworkHH - - - - - - - - - - - - - - - - - - 0 0
DrivF - - - - - - - - - - - 0 1 - - - - - - -
Dist3 - - - - - - - - - - - - - - - - 0 1-5 - -
Urban - - - - - - - - - - - - - - - - 0 0 1-4 1-4
Time1F 0-1 2-4 2-4 2-4 - - - - - - - - - - - - - - - -
Comp - - - - - - 2,4 2,4 3 2 3,4 3,4 - - - - - - -
Time3F - - - - - - 0-3 4 4 4 - - - - - - - - - -
nsoc - - - - - - - - - - - - - - - - 0 0 0 0
WstatF - - - - - - - - - - - - - 0,1 2 - - - - -
Time5F - - - - 0-2 3 4 4 4 4 - - - - - - - - - -
Time2C - 0-2 0-2 3-4 - - - - - - - - - - - - - - - -
Yes 0.122 0 0.088 0.011 0.016 0.075 0.055 0.002 0.033 0.029 0.117 0.154 0.255 0.126 0.039 0.167 0.061 0.176 0.209 0.308
No 0.878 1 0.912 0.989 0.984 0.925 0.945 0.998 0.967 0.971 0.883 0.846 0.745 0.874 0.961 0.833 0.939 0.824 0.791 0.692
N 115 86 193 265 428 107 271 435 91 381 205 117 423 333 103 635 114 131 254 987
R281 R282 R283 R284 R285 R286 R287 R288 R289 R290 R291 R292 R293 R294 R295 R296 R297 R298 R299 R300
216
TABLE AII-1.16 Household Activity Participation
HHact 5 5 5 5 5 5 6 6 6 6 6 6 6 6 6 6 6 6 6 6
Day 6 6 6 6 6 6 0 0 0 0 0 1,2,3 1,2,3 1,2,3 1,2,3 1,2,3 1,2,3 1,2,3 1,2,3 1,2,3
nbr - - - - - - - - - - - - - - - - - - - 0
nsh1 - - - - - - - - - - - - 0 1-5 1-5 - - - - -
Dist1 - - 0-3 4-5 - - - - - - - - - - - - - - - -
SEC - - - - - - - - - - - 0-1 2-3 2-3 2-3 - - - - -
NworkHH 1-4 - - - - - 0 0 0 1-4 1-4 0 0 0 0 1-4 1-4 1-4 1-4 1-4
yBusiM - - - - - - - - - 0 1 - - - - - - - - -
AgeM - 0 1 1 2 3-4 - - - - - - - - - - - - - -
Dist2 - - - - - - - - - - - - - - - - 0-2 0-2 3-5 3-5
Dist3 - - - - - - 0-2 3-5 3-5 - - - - 0 1-5 - - - - -
Urban 1-4 - - - - - - - - - - - - - - - - - - -
DurM - - - - - - - - - - - - - - - - - - 0-1 2-4
Comp - - - - - - - 2,4 3 - - - - - - 2,4 3 3 3 3
nsoc 0 1-2 1-2 1-2 1-2 1-2 - - - - - - - - - 0 0 0 0 0
Time1M - - - - - - - - - - - - - - - 0 0 0 0 0
yWorkF - - - - - - - - - - - - - - - - 0 1 - -
Yes 0.147 0.267 0.211 0.08 0.233 0.037 0.029 0.063 0.148 0.005 0.027 0.042 0.123 0.12 0.045 0.008 0.083 0.031 0.062 0.004
No 0.853 0.733 0.789 0.92 0.767 0.963 0.971 0.937 0.852 0.995 0.973 0.958 0.877 0.88 0.955 0.992 0.917 0.969 0.938 0.996
N 95 75 90 113 103 82 308 492 81 1109 149 1365 600 83 619 760 120 194 81 525
R301 R302 R303 R304 R305 R306 R307 R308 R309 R310 R311 R312 R313 R314 R315 R316 R317 R318 R319 R320
217
TABLE AII-1.17 Household Activity Participation
HHact 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6
Day 1,2,3 1,2,3 1,2,3 1,2,3 1,2,3 4 4 4 4 5 5 5 5 5 5 5 5 5 6 6
nbr 1-5 - - - - - - - - - - - - - - - - - - -
Child - - - - - - - - - - - - - - 0 1,3,2 - - - -
nsh1 - - - - - - - - - - 0 0 1 1 2-5 2-5 2-5 2-5 - -
nshn - - - - - - - - - - 0 1-3 - - - - - - - -
DurHH - - - - - - - - - - - - - - - - - - 0 0
Dist1 - - - - - - - - - - - - - - - - - - 0 0
SEC - - - - - - - - - - - - 0-2 3 - - - - - -
NworkHH 1-4 1-4 1-4 1-4 1-4 - - - - - - - - - - - - - - -
DrivF - - - - - 0 1 0 1 1 1 1 1 1 1 1 - -
yWorkM - - - - - 0 0 1 - - - - - - - - - - - -
AgeM - - - - - - - - - - - - - - 0-1 0-1 2-4 2-4 - -
Dist2 3-5 - - - - - - - - - - - - - - - - - - -
nlei - - - - - 0 0 0 1-2 - - - - - - - - - - -
DurM 2-4 - - - - - - - - - - - - - - - - - - -
Comp 3 3 - - - - - - - - - - - - - - - - - -
nEmp1 - - - - - - - - - - - - - - - - - - 0-1 2-3
Dist4 - - - - - - - - - - - - - - - - 0-3 4-5 - -
nsoc 0 0 0 0 1-2 - - - - - - - - - - - - - - -
Time1M 0 1 2 3-4 - - - - - - - - - - - - - - -
Yes 0.035 0.039 0 0.026 0.115 0.035 0.086 0.032 0.21 0.044 0.194 0.074 0.033 0.096 0.215 0.107 0.104 0 0.089 0.2
No 0.965 0.961 1 0.974 0.885 0.965 0.914 0.968 0.79 0.956 0.806 0.926 0.967 0.904 0.785 0.893 0.896 1 0.911 0.8
N 141 488 287 1056 87 260 853 920 124 273 439 135 209 166 163 196 77 79 79 130
R321 R322 R323 R324 R325 R326 R327 R328 R329 R330 R331 R332 R333 R334 R335 R336 R337 R338 R339 R340
218
TABLE AII-1.18 Household Activity Participation
HHact 6 6 6 6 6 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7
Day 6 6 6 6 6 0,4 0,4 0,4 0,4 0,4 0,4 0,4 0,4 0,4 0,4 0,4 0,4 0,4 0,4 1,2,3 1,2,3 1,2,3 1,2,3
nbr - - - - - - - - - - - - - - - - - - - 0 0 1-5 -
DurHH 0 0 0 0 1-4 - - - - - - - - - - - - - - - - - -
ModeHH - - - - - 0 0 0 0 0 1,3 1,3 1,3 1,3 1,3 2,4 2,4 2,4 2,4 0 0 0 0
Dist1 0 1-5 1-5 1-5 - - - - - - - - - - - - - - - 0-3 4-5 - -
ntou - - - - - 0 0 0 0 1-2 - - - - - - - - - 0 0 0 1-2
yWorkM - - - - - - - - - - - - - 0 1 - - - - - - - -
Ncar - - - - - - - - - - - - - - - 0-1 0-1 0-1 2 - - - -
Dist2 - - - - - - 0-1 2-5 - - 0-3 0-3 4 5 5 - - - - - - - -
Urban - 0-1 2-4 - - 0 1-2 1-2 3-4 - - - - - - - - - - - - - -
nEmp2 - - - - - - - - - - 0 1-5 - - - - - - - - - - -
SizePop - - - - - - - - - - - - - - - 0 1 2-5 - - - - -
Comp - 2,3 2,3 4 - - - - - - - - - - - - - - - - - - -
nEmp1 4-5 - - - - - - - - - - - - - - - - - - - - - -
Yes 0.074 0.24 0.143 0.252 0.028 0.031 0.039 0.127 0.047 0.164 0.012 0 0.027 0.03 0.005 0.005 0.005 0.003 0.051 0.037 0.068 0 0.162
No 0.926 0.76 0.857 0.748 0.972 0.969 0.961 0.873 0.953 0.836 0.988 1 0.973 0.97 0.995 0.995 0.954 0.997 0.949 0.963 0.932 1 0.838
N 231 204 722 445 106 223 129 221 1169 128 83 603 185 100 603 207 108 304 118 1013 1259 230 142
R341 R342 R343 R344 R345 R346 R347 R348 R349 R350 R351 R352 R353 R354 R355 R356 R357 R358 R359 R360 R361 R362 R363
219
TABLE AII-1.19 Household Activity Participation
HHact 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7
Day 1,2,3 1,2,3 1,2,3 1,2,3 1,2,3 1,2,3 5 5 5 5 5 5 5 5 6 6 6 6 6 6 6 6 6
Child - - - - - - - - - - - - - - 0,1 0,1 0,1 0,1 0,1 0,1 2,3 2,3 2,3
nsh1 0-1 2-5
- - - - 0 0 1-5 1-5 1-5
- - - - - - - - - - - -
ModeHH 1,3,2 1,3,2 1,3,2 1,3,2 1,3,2 4
- - - - - - - - - - - - - - - - -
Dist1 - - - - - - - - - - - - - - - - - - - - 0-1 2-4 5
SEC - - - 0-1 2-3
- - - - - - - - - 0,1 0,1 2-3 2-3 2-3 2-3
- - -
ntou - - - - - - 0 0 0 0 0 0 0 1-2
- - - - - - - - -
DrivF - - - - - - 0 1
- - - - - - - - - - - - - - -
Ncar - - - - - - - - 0-1 2
- - - - - - - - - - - - -
WstatM 0,2 0,2 0,2 1 1
- - - - - - - - - - - 0 0 1,2 1,2
- - -
nsoc - - - - - - 0 0 0 0 0 1-2
- - - - - - - - - - -
nEmp3 - - - - - - - - - - - - - 0-2 3-5
- - - - - - -
WstatF - - - - - - - - 0,1 0,1 2
- - - - - - - 0,1 2
- - -
yWorkF - - - - - - 0 0 0 0 0 0 1
- - - - - - - - - -
yOthF 0 0 1
- - - - - - - - - - - - - - - - - - - -
Yes 0 0.008 0.011 0.037 0 0.008 0.037 0.101 0.048 0.006 0.083 0.02 0 0.182 0.145 0.053 0.132 0.263 0.174 0.099 0.03 0.148 0.064
No 1 0.992 0.989 0.963 1 0.992 0.963 0.899 0.952 0.994 0.917 0.98 1 0.818 0.855 0.947 0.868 0.737 0.826 0.901 0.97 0.852 0.936
N 2105 258 94 82 209 898 107 337 374 175 180 250 119 88 380 187 76 259 391 222 99 115 140
R364 R365 R366 R367 R368 R369 R370 R371 R372 R373 R374 R375 R376 R377 R378 R379 R380 R381 R382 R383 R384 R385 R386
220
TABLE AII-2.1 Household Activity Allocation
durM 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
nsh1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
nbr 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
durF 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
yBusiM 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
nshn 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1-3
wstatF 0,1 0,1 0,1 0,1 0,1 0,1 0,1 0,1 0,1 0,1 0,1 0,1 0,1 0,1 2 2 2 2 2 -
Acty 1 1 1 2,4 2,4 2,4 2,4 2,4 2,4 2,4 2,4 2,4 3 3 - - - - - -
Child 0,3 1,2 1,2 - - - - - - - - - - - - - 0 - 1,3,2 -
Comp - 2 3,4 - - - - - - - - - - - - - - - - -
nser - - - 0 0 0 0 0 0 0 0 1-3 - - - - - - - -
SEC - - - 0 0 1 1 1 1 2-3 2-3 - - - - - - - - -
SizePop - - - 0-3 4-5 - - - - - - - - - - - - - - -
AgeM - - - - - 0-2 0-2 3-4 3-4 - - - - - - - - - - -
drivF - - - - - 0 1 - - - - - - - - - - - - -
ncar - - - - - - - 0 1 - - - - - - - - -
Urb - - - - - - - - - 0-1 2-4 - - - - - - - - -
nEmp2 - - - - - - - - - - 0-1 2-5 - - 0-1 2-5 - -
wstatM - - - - - - - - - - - - - - 0-1 0-1 2 2 2 -
Day - - - - - - - - - - - - - - 0,1,2,3,6 4,5 - - - -
Male 0.514 0.304 0.512 0.487 0.333 0.398 0.336 0.388 0.39 0.404 0.468 0.387 0.229 0.38 0.311 0.116 0.247 0.341 0.4 0.326
Female 0.204 0.565 0.317 0.258 0.179 0.216 0.391 0.359 0.226 0.25 0.287 0.493 0.307 0.231 0.485 0.524 0.376 0.179 0.425 0.568
Both 0.282 0.13 0.171 0.255 0.487 0.386 0.273 0.252 0.384 0.346 0.244 0.12 0.464 0.389 0.205 0.361 0.376 0.48 0.175 0.105
N 245 92 82 353 78 88 271 103 461 280 1023 75 179 208 132 147 93 123 80 190
R1 R2 R3 R4 R5 R6 R7 R8 R9 R10 R11 R12 R13 R14 R15 R16 R17 R18 R19 R20
221
TABLE AII-2.2 Household Activity Allocation
durM 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
nsh1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1
nbr 0 0 0 0 0 0 0 1 1 1 1 2 2 2 3-5 3-5 - 0 0 0
durF 0 0 1 2 3 3 4 - - - - 0-1 0-1 2-4 - - - - - -
yBusiM 1 1 - - - - - - - - - - - - - - - 0 0 0
wstatF - - - - - - - - - - - - - - - - - 0,1 2 2
Acty - - - - - - - - - - - - - - - - - 1,2 1,2 1,2
Child - - - - - - - 0 1,2,3 1,2,3 - 0-3 1-2 - - - - - - -
SizePop - - - - - - - - - - - - - - - - - - 0-1 2-5
ncar 0-1 2 - - - - - - - - - - - - - - - - - -
nEmp2 - - - - - - - - 0-2 3-5 - - - - - - - - - -
time1C - - - - 0-2 3-4 - - - - - - - - - - - - - -
yWorkF - - - - - - - - - - - - - - 0 1 - - - -
time1F - - - - - - - - - - - - - - - - 0-3 4 4 4
durHH - - - - - - - 0-1 0-1 0-1 2-4 - - - - - - - - -
Male 0.407 0.196 0.444 0.51 0.702 0.524 0.788 0.511 0.205 0.39 0.764 0.415 0.222 0.696 0.156 0.633 0.661 0.465 0.462 0.229
Female 0.458 0.768 0.477 0.351 0.228 0.435 0.185 0.38 0.712 0.568 0.236 0.5 0.763 0.293 0.834 0.367 0.333 0.412 0.385 0.542
Both 0.136 0.036 0.079 0.139 0.07 0.035 0.026 0.109 0.082 0.042 0 0.085 0.015 0.011 0.01 0 0.006 0.123 0.154 0.229
N 118 138 151 151 114 85 189 229 219 118 110 106 194 92 199 90 177 876 78 83
R21 R22 R23 R24 R25 R26 R27 R28 R29 R30 R31 R32 R33 R34 R35 R36 R37 R38 R39 R40
222
TABLE AII-2.3 Household Activity Allocation
durM 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1
nsh1 1 1 1 1 2 2 3-5 3-5 3-5 0 0 1-5 - - - - - - -
nbr 0 0 1 2-5 - - - - - 0 0 0 1 2-5 - - - 0 1-5
durF - - - - - - - - - 0-1 0-1 0-1 0-1 0-1 2 3-4 - - -
yBusiM 0 1 - - - - - - - 0 0 0 0 0 0 0 1 1 1
wstatF - - - - 0,1 2 0,1 0,2 2 0,1 2 - - - - - - 0,1 0,1
Acty 3,4 - - - - - - - - - - - - - - - - - -
Dist1 - - - - - - 0-3 4-5 - - - - - - - - - - -
time1F 4 - - - - - - - - - - - - - - - 0-1 2-4 2-4
Male 0.394 0.268 0.405 0.176 0.311 0.416 0.284 0.19 0.522 0.445 0.198 0.249 0.271 0.147 0.49 0.691 0.314 0.115 0.037
Female 0.525 0.695 0.583 0.775 0.627 0.468 0.679 0.81 0.435 0.411 0.581 0.72 0.712 0.853 0.402 0.272 0.676 0.858 0.963
Both 0.081 0.037 0.012 0.049 0.062 0.117 0.037 0 0.043 0.144 0.221 0.032 0.017 0 0.108 0.037 0.01 0.027 0
N 617 82 84 102 598 154 190 211 92 299 86 189 118 136 102 136 105 261 161
R41 R42 R43 R44 R45 R46 R47 R48 R49 R50 R51 R52 R53 R54 R55 R56 R57 R58 R59
223
TABLE AII-2.4 Household Activity Allocation
durM 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 3 3 3
nbr - 0 0 0 0 1-5 1-5 1-5 1-5 0 0 0
durF - 0 0 0 0 0 0 0 0 1-2 1-2 1-2 3 3 4 0 0 0
yBusiM 1 - - - - - - - - - - - - - - - - -
wstatF 2 - - - - 0,1 0,1 0,1 2 - - - - - - - - -
Urb - - - - - - - - - - - - - - - 0-3 4 -
time1F 2-4 - - - - - - - - - - - - - - - - -
time4M - 0 0 1-3 4 0-1 0-1 2-4 - - - - - - - 0-1 0-1 2-4
time5M - 0-2 3-4 - - - - - - - - - - - - - - -
modeF - - - - - - - - - 0,1,4 2,3 2,3 - - - - - -
time4F - - - - - - - - - - - - 0-1 2-4 - - - -
Dist3 - - - - - 0-2 3-5 - - - 0-4 5 - - - - - -
Male 0.199 0.123 0.226 0.286 0.178 0.052 0.005 0.099 0.212 0.333 0.095 0.25 0.433 0.227 0.594 0.106 0.154 0.301
Female 0.789 0.845 0.77 0.639 0.779 0.948 0.995 0.901 0.788 0.667 0.853 0.74 0.433 0.667 0.338 0.839 0.831 0.667
Both 0.012 0.032 0.004 0.075 0.043 0 0 0 0 0 0.053 0.01 0.134 0.107 0.068 0.055 0.014 0.032
N 166 252 226 147 208 116 185 121 132 132 95 96 97 75 133 199 356 93
R60 R61 R62 R63 R64 R65 R66 R67 R68 R69 R70 R71 R72 R73 R74 R75 R76 R77
224
TABLE AII-2.5 Household Activity Allocation
durM 3 3 3 3 3 3 3 4 4 4 4 4 4 4 4 4 4
nsh1 - - - - - - - - - - - - - - - - -
nbr 1-5 1-5 1-5 - - - - 0 0 0 1-5 1-5 1-5 - - - -
durF 0 0 0 1-2 1-2 3 4 0 0 0 0 0 0 1-3 1-3 1-3 4
wstatF 0 1,2 1,2 - - - - - - - - - - - - - -
SizePop - - - - - - - - - - 0 1-5 1-5 - - - -
ncar - 0-1 2 - - - - - - - - - - 0-1 2 -
Urb - - - - - - - - - - - - - 0-1 2-4 2-4 -
time4M - - - - - - - 0 0 1-4 - - - - - - -
time5M - - - - - - - 0-2 3-4 - - - - - - - -
Dist3 - - - - - - - - - - - 0-3 4-5 - - - -
AgeF - - - 0 1-4 - - - - - - - - - - - -
Male 0.023 0.057 0.177 0.307 0.139 0.293 0.452 0.083 0.12 0.206 0 0.023 0.098 0.0284 0.175 0.062 0.327
Female 0.977 0.943 0.81 0.667 0.855 0.672 0.423 0.912 0.831 0.724 1 0.977 0.092 0.695 0.817 0.932 0.591
Both 0 0 0.013 0.027 0.006 0.034 0.125 0.004 0.049 0.065 0 0 0 0.021 0.008 0.006 0.082
N 171 106 79 75 173 116 104 480 142 107 198 171 92 95 126 176 110
R78 R79 R80 R81 R82 R83 R84 R85 R86 R87 R88 R89 R90 R91 R92 R93 R94
225
TABLE AIII-1 Duration
acty 1 2,4,7 2,4,7 2,4,7 2,4,7 2,4,7 2,4,7 3 5 5 5 5 6 6 6 6
durtot - 0 0 0 0 0 1-4 - 0-1 0-1 2-4 2-4 0-1 0-1 0-1 2-4
availT3 - 0-1 2-4 2-4 2-4 2-4 - - 0-3 4 - - - - - -
AgeM - - 0-1 2 2 3-4 - - - - - - - - - -
AgeF - - - 0-1 2-4 - - - - - - - - - -
ncar - - - - - - - - - - 0-1 2 0-1 0-1 2 -
Urban - - - - - - - - - - 0-2 3-4 - -
m 35.25 47.264 82.948 144.45 85.118 74.524 88.126 56.746 105.23 157.083 144.151 174.736 171.6 134 179.5 120.4
S 50.632 43.87 89.336 216.77 101.59 92.196 107.09 81.441 75.278 80.994 109.767 114.721 156 113 158.9 86.86
N 108 144 495 75 245 584 95 497 121 952 205 121 168 272 197 337
R1 R2 R3 R4 R5 R6 R7 R8 R9 R10 R11 R12 R13 R14 R15 R16
226
TABLE AIII-2.1 Start-Time
availT2 0 0 0 0 1-4 1-4 1-4 1-4 1-4 1-4 1-4 1-4 1-4 1-4 1-4 1-4 1-4
acty 1,4,2,3 1,4,2,3 5,6,7 5,6,7 1,6 1,6 1,6 1,6 1,6 1,6 2,3,4 2,3,4 2,3,4 2,3,4 2,3,4 2,3,4 2,3,4
child 0 1,2,3 - - - - - - - - - - - - - - -
availT4 - - 0-2 3-4 - - - - - - - - - - - - -
nlei - - - - 0 0 0 0 0 1-2 - - - - - - -
Day - - - - 0,2,6 0,2,6 0,2,6 0,2,6 1,3,4,5 - - - - - - - -
durasi - - - - 0-2 0-2 3 4 - - 0 0 1-4 1-4 1-4 1-4
Comp - - - - 2-3 4 - - - - - - - - - - 2,4
durtot - - - - - - - - - - 0 0 0 0 0 0 1-4
ageM - - - - - - - - - - 0-2 3-4 - - - - -
SEC - - - - - - - - - - - - 0-1 2-3 2-3 2-3 -
Dist3 - - - - - - - - - - - - - 0-2 3-5 3-5 -
ncar - - - - - - - - - - - - - - 0-1 2 -
m 1005.3 890.9 1172.3 1066.82 824.88 737.25 708.9 782.7 854.06 989.69 797.87 733.39 713.71 757.13 710.38 746.14 783.54
S 164.27 247.09 96.88 152.09 229.28 204.25 190.75 223.36 219.77 176.59 169.68 161.7 142.21 142.76 133.48 135.52 137.51
N 130 80 216 96 112 94 115 87 399 140 113 84 529 209 206 131 237
R1 R2 R3 R4 R5 R6 R7 R8 R9 R10 R11 R12 R13 R14 R15 R16 R17
227
TABLE AIII-2.2 Start-Time
availT2 1-4 1-4 1-4 1-4 1-4 1-4 1-4 1-4 1-4 1-4 1-4 1-4 1-4 1-4
acty 2,3,4 5 5 5 5 5 5 5 5 7 7 7 7 7
Day - 0-6 0-6 0-6 1,3,2 4,5 4,5 - - - - - - -
durasi - - - - - 0-3 4 - - 0-1 2-4 0-1 0-1 2-4
Comp 3 - - - - - - - - - - - - -
durtot 1-4 - - - - - - - - 0 0 1-4 1-4 1-4
ageM - - - - - - 0-1 2-4 - - - - - -
SEC - 0-1 2-3 2-3 - - - - - - - - - -
dursoc - 0-1 0-1 0-1 0-1 0-1 0-1 0-1 2-4 - - - - -
nEmp1 - - 0-1 2-5 - - - - - - - - - -
m 841.17 813.79 884.32 827.05 875.26 892.54 1035.2 949.53 1016.8 856.75 750.47 900.86 964.04 840.67
S 126.03 164.09 157.52 171.48 211.28 195.8 186.1 227.6 155.42 151.69 125.54 176.61 166.45 131.46
N 101 159 163 144 247 188 85 78 122 183 116 131 75 78
R18 R19 R20 R21 R22 R23 R24 R25 R26 R27 R28 R29 R30 R31
228
TABLE AIV-1.1 Location Choice Model – Independent Activity – 3 alternatives
LvoisLna 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1
Vohtt 0 0 1 1 2-3 2-3 2-3 2-3 2-3 4 4 4 4 4 - - - - - -
Nahtt 0-2 3-4 - - - - - - - - - - - - - - - - - -
Nagord - - 0 1-4 - - - - - - - - - - - - - - - -
Vosize - - - - 0-2 0-2 0-2 3-4 3-4 - - - - - - 0-2 3-4 - - -
NaH - - - - 0 1 1 - - - - - - - - - - - - 0
NaSize - - - - - 0-1 2-4 - - - - - - - - - - - - -
VoisA - - - - - - - 0 1 - - 0 1 - - - - - - -
Voty - - - - - - - - - 0,1 2 3 3 3 - - - - - -
MxSizeD4 - - - - - - - - - - - 0-2 0-2 3-5 - - - - - -
Adur - - - - - - - - - - - - - - 0 0 0 0 0 0
Aty - - - - - - - - - - - - - - 0,1,10,3 0,1,10,3 0,1,10,3 2,5 2,5 4,6,7,9
Vogord - - - - - - - - - - - - - - 0 1-4 1-4 - - 0
Comp - - - - - - - - - - - - - - - - - 0,4,1 2,3 -
same as
previous 0.237 0.179 0.421 0.4 0.275 0.257 0.158 0.346 0.41 0.099 0.092 0.219 0.328 0.453 0.792 0.578 0.642 0.497 0.678 0.462
same as next 0.289 0.128 0.368 0.183 0.087 0.151 0.27 0.137 0.049 0.113 0.021 0.08 0.017 0.047 0.002 0.6 0 0 0 0
other 0.474 0.694 0.24 0.417 0.638 0.592 0.566 0.517 0.542 0.789 0.887 0.701 0.655 0.5 0.205 0.427 0.308 0.503 0.322 0.538
N 97 196 95 240 207 331 152 315 144 142 142 187 116 128 307 216 169 169 326 169
R1 R2 R3 R4 R5 R6 R7 R8 R9 R10 R11 R12 R13 R14 R15 R16 R17 R18 R19 R20
229
TABLE AIV-1.2 Location Choice Model – Independent Activity – 3 alternatives
LvoisLna 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
Vosize - - - - 0-3 4 - - - - - - - - - - 0-1
NaH 1 1 1 1 - - - - - - - - - - - - -
Adur 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1
Aty 4,6,7,9 4,6,7,9 4,6,7,9 4,6,7,9 4,6,7,9 4,6,7,9 4,6,7,9 4,6,7,9 4,6,7,9 4,6,7,9 8 8 8 0,1,10,3,5,8 0,1,10,3,5,8 0,1,10,3,5,8 2,4,6,7,9
Vogord 0 0 0 0 1 1 2-4 2-4 2-4 2-4 - - - 0 0 0 0
Comp - - - - - - - 0,1 2,4,3 - - - - - - - -
pAge 0,1 0,1 2,4,3 2,4,3 - - - - - - - - - - 0,3,4 1,2 -
Tavail 0,1 2-4 0-2 3-4 - - - - - - - - - - - - -
Naty - - - - - - 0 0 0 1,2,3 - - - - - - -
Driver - - - - - - 0 1 1 - - - - - - - -
SEC - - - - - - - - - - 0 1,2,3 1,2,3 - - - -
Wstat - - - - - - - - - - - 0 1,2 - - - -
Gend - - - - - - - - - - - - - 0 1 1 0
same as
previous 0.675 0.527 0.789 0.654 0.369 0.598 0.49 0.241 0.413 0.14 0.657 0.852 0.759 0.723 0.722 0.529 0.454
same as next 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
other 0.325 0.473 0.211 0.346 0.631 0.402 0.51 0.759 0.587 0.86 0.343 0.148 0.241 0.277 0.278 0.471 0.546
N 154 226 180 130 401 107 149 145 286 164 181 310 224 329 90 155 205
R21 R22 R23 R24 R25 R26 R27 R28 R29 R30 R31 R32 R33 R34 R35 R36 R37
230
TABLE AIV-1.3 Location Choice Model – Independent Activity – 3 alternatives
LvoisLna 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
Vosize 2-4 - - 0-1 2-4 - - 0-1 2-4 - - - - - - - -
Adur 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2
Aty 2,4,6,7,9 2,4,6,7,9 0,1,10,2 3,4,8 3,4,8 5,7,9,6 5,7,9,6 5,7,9,6 5,7,9,6 0,1,10,8,2,3 4,6,9 5,7 - - - - -
Vogord 0 0 1-2 1-2 1-2 1-2 1-2 - - 3-4 3-4 3-4 0 0 0 0 0
Comp - - - - - - - - - - - - - - - 0,1,3 2,4
pAge - - - - - 0,3,2 1,4 - - - - - - - - - -
Tavail - - - - - - - - - - - - 0-2 3-4 3-4
Wstat - - - - - - - - - - - - 0 1,2 - - -
Gend 0 1 - - - - - - - - - - - - - - -
Urb - - - - - 0,1,2 0,1,2 3,4 3,4 - - - - - - - -
Child - - - - - - - - - - - - 0 0 0 0 0
Day - - - - - - - - - - - - 0,4 0,4 1,3,2,5,6 1,3,2,5,6 1,3,2,5,6
same as
previous 0.599 0.362 0.642 0.353 0.57 0.152 0.308 0.25 0.447 0.405 0.118 0.216 0.597 0.427 0.417 0.224 0.378
same as next 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
other 0.401 0.638 0.567 0.647 0.43 0.848 0.692 0.75 0.553 0.595 0.882 0.784 0.403 0.573 0.583 0.776 0.622
N 202 199 485 306 179 132 120 128 132 252 119 125 139 103 312 152 156
R38 R39 R40 R41 R42 R43 R44 R45 R46 R47 R48 R49 R50 R51 R52 R53 R54
231
TABLE AIV-1.4 Location Choice Model – Independent Activity – 3 alternatives
LvoisLna 1 1 1 1 1 1 1 1 1 1 1 1 1
Nagord - - - - 0 1-4 - - - - - - -
Vosize - - - - - - 0 1-4 - - - - -
Adur 2 2 2 2 2 2 2 2 2 2 2 2 2
Aty - - 0,1,10,8,2,5 0,1,10,8,2,5 0,1,10,8,2,5 3,6,9,7,4 - - - - - - -
Vogord 0 0 1 1 1 1 1 1 1 2-4 2-4 2-4 2-4
Comp - - 0,2,3 0,2,3 0,2,3 0,2,3 1,4 1,4 - - - - -
Tavail - - 0-3 0-3 0-3 0-3 0-3 0-3 4 - - 0 1-4
SEC - - - 0,1,2 3 - - - - - - - -
Wstat - - - - - - - - - 0 1,2 - -
Urb - - 0,2,3 1,4 1,4 - - - - - - - -
Child 1,2 3 - - - - - - - 0 0 1,2,3 1,2,3
same as
previous 0.517 0.314 0.347 0.623 0.394 0.24 0.153 0.32 0.213 0.226 0.14 0.44 0.289
same as next 0 0 0 0 0 0 0 0 0 0 0 0 0
other 0.483 0.686 0.653 0.377 0.606 0.76 0.847 0.68 0.787 0.774 0.86 0.56 0.711
N 575 159 170 154 109 167 111 122 197 561 350 159 402
R55 R56 R57 R58 R59 R60 R61 R62 R63 R64 R65 R66 R67
232
TABLE AIV-2.1 Location Choice Model – Independent Activity – 25 alternatives
LvoisLna 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1
Nagord 0 0 1-3 1-3 1-3 1-3 1-3 1-3 4 - - - - - - - - - - -
Naty 0,2 1,3 - - - - - - - - - - - - - - - - - -
NaSize - - 0-1 0-1 0-1 2 3 4 - - - - - - - - - - - -
pAge - - 0,4,1 0,4,1 2,3 - - - - - - - - - - - - - - -
VoSize - - 0-1 2-4 - - - - - - - 0 1-4 1-4 1-4 1-4 0-1 0-1 0-1 0-1
Vogord - - - - - - - - - 0 0 0 0 0 0 0 0 0 0 0
Urb - - - - - - - - - 0,1,2 3 4 4 4 4 4 4 4 4 4
Adur - - - - - - - - - - - 0-1 0-1 0-1 0-1 0-1 2 2 2 2
Comp - - - - - - - - - - - - 0,1,3 0,1,3 2 4 - - - -
Aty - - - - - - - - - - - - - - - - 0,1,10,4,5,9 2,6,8,3,7 2,6,8,3,7 2,6,8,3,7
Wstat - - - - - - - - - - - - - - - - - 0,2 0,2 1
SEC - - - - - - - - - - - - - - - - - 0,1 2,3
Day - - - - - - - - - - - - - - - - - - - -
Ncar - - - - - - - - - - - - - - - - - - - -
Pwstat - - - - - - - - - - - - 0,1 2 - - - - - -
Z1 0.069 0.078 0.093 0.093 0.113 0.074 0.072 0.03 0.016 0.027 0.006 0.025 0 0.014 0 0.011 0.052 0 0 0
Z2 0.056 0.055 0.056 0.023 0.163 0.107 0.048 0.107 0.033 0.047 0.038 0.025 0.034 0.027 0.026 0 0.006 0.008 0 0
Z3 0.089 0.133 0.131 0.085 0.087 0.149 0.048 0.148 0.085 0.081 0.026 0.014 0 0.014 0.043 0.011 0.039 0 0.014 0.01
Z4 0.046 0.055 0.098 0.093 0.05 0.041 0.084 0.059 0.077 0.081 0.045 0.025 0.017 0.027 0.026 0.078 0.032 0.008 0.007 0.01
Z5 0.056 0.039 0.145 0.116 0.062 0.05 0.133 0.118 0.203 0.027 0.032 0.022 0 0.014 0 0.022 0 0 0 0.019
Z6 0.056 0.039 0.061 0.023 0.013 0.041 0.012 0.036 0.02 0.014 0.064 0.084 0.026 0.007 0.052 0.056 0.039 0 0.014 0.01
Z7 0.043 0.023 0.019 0 0.075 0.041 0.012 0.018 0.049 0.034 0.032 0.025 0.026 0.02 0.069 0.033 0.045 0.016 0 0.058
Z8 0.056 0.016 0.042 0.039 0.025 0.058 0.048 0.03 0.053 0.135 0 0.025 0.017 0.007 0.017 0.033 0.052 0.008 0.007 0.029
Z9 0.026 0.086 0.023 0.054 0 0.041 0.048 0.012 0.045 0.027 0.058 0.039 0.051 0.027 0 0.089 0.039 0.025 0.022 0.049
Z10 0.026 0.008 0.042 0.116 0.087 0.041 0.096 0.077 0.085 0.014 0.038 0.017 0 0 0.026 0.011 0 0.016 0.022 0.019
Z11 0.053 0.023 0.033 0.023 0.013 0.025 0.036 0.018 0.02 0.047 0.038 0.081 0.026 0.02 0.034 0.033 0.052 0.008 0.022 0.058
Z12 0.049 0.016 0.033 0 0.062 0.025 0.048 0.03 0.012 0.027 0.032 0.034 0.043 0.054 0.017 0.022 0.052 0.098 0.043 0.029
Z13 0.039 0.008 0.005 0.039 0.013 0.05 0 0.018 0.045 0.054 0.051 0.031 0.085 0.047 0.078 0.122 0.026 0.025 0.007 0
Z14 0.03 0.031 0.019 0.008 0.025 0.008 0 0.018 0.016 0.027 0.026 0.031 0.068 0.047 0.112 0.033 0.019 0.016 0.007 0.039
Z15 0.02 0.078 0.033 0.047 0.037 0.017 0.036 0.041 0.053 0.047 0.038 0.014 0.017 0.041 0.017 0.011 0.045 0.049 0.029 0.039
R1 R2 R3 R4 R5 R6 R7 R8 R9 R10 R11 R12 R13 R14 R15 R16 R17 R18 R19 R20
233
TABLE AIV-2.1 (cont.)
LvoisLna 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1
Nagord 0 0 1-3 1-3 1-3 1-3 1-3 1-3 4 - - - - - - - - - - -
Naty 0,2 1,3 - - - - - - - - - - - - - - - - - -
NaSize - - 0-1 0-1 0-1 2 3 4 - - - - - - - - - - - -
pAge - - 0,4,1 0,4,1 2,3 - - - - - - - - - - - - - - -
VoSize - - 0-1 2-4 - - - - - - - 0 1-4 1-4 1-4 1-4 0-1 0-1 0-1 0-1
Vogord - - - - - - - - - 0 0 0 0 0 0 0 0 0 0 0
Urb - - - - - - - - - 0,1,2 3 4 4 4 4 4 4 4 4 4
Adur - - - - - - - - - - - 0-1 0-1 0-1 0-1 0-1 2 2 2 2
Comp - - - - - - - - - - - - 0,1,3 0,1,3 2 4 - - - -
Aty - - - - - - - - - - - - - - - - 0,1,10,4,5,9 2,6,8,3,7 2,6,8,3,7 2,6,8,3,7
Wstat - - - - - - - - - - - - - - - - - 0,2 0,2 1
SEC - - - - - - - - - - - - - - - - - 0,1 2,3 -
Pwstat - - - - - - - - - - - - 0,1 2 - - - - - -
Z16 0.056 0.008 0.009 0.008 0.05 0 0.024 0.006 0.004 0.014 0.032 0.073 0.085 0.027 0.069 0.011 0.09 0 0.145 0.039
Z17 0.039 0.031 0.009 0.008 0.025 0.05 0.012 0.012 0.028 0.007 0.006 0.073 0.103 0.027 0.078 0.056 0.071 0.098 0.058 0.039
Z18 0.043 0.016 0.019 0.016 0.013 0.041 0 0.036 0.024 0.014 0.045 0.053 0.077 0.054 0.034 0.033 0.052 0.057 0.036 0.01
Z19 0.02 0.062 0.014 0.008 0 0.017 0.048 0.024 0.012 0.02 0.038 0.053 0.06 0.095 0.06 0.067 0.039 0.107 0.036 0.049
Z20 0.016 0.008 0.014 0.008 0.025 0 0.06 0.006 0.037 0.068 0.058 0.053 0.034 0.047 0.026 0.022 0.032 0.025 0.058 0.117
Z21 0.023 0.047 0.037 0.008 0 0.025 0.012 0.041 0.012 0.02 0.051 0.079 0.103 0.054 0.06 0.089 0.019 0.115 0.065 0.087
Z22 0.03 0.031 0.005 0.008 0 0.025 0.012 0.047 0.012 0.02 0.064 0.031 0.026 0.074 0.095 0.022 0.045 0.107 0.094 0.078
Z23 0.023 0.055 0.019 0.07 0.025 0.033 0.048 0.03 0.024 0.047 0.064 0.045 0.009 0.108 0.017 0.044 0.032 0.033 0.138 0.107
Z24 0.013 0.039 0.014 0.031 0.025 0.008 0.024 0.006 0.016 0.034 0.071 0.022 0.043 0.108 0.026 0.033 0.065 0.033 0.101 0.058
Z25 0.023 0.016 0.028 0.078 0.013 0.033 0.0336 0.036 0.016 0.068 0.045 0.022 0.051 0.041 0.017 0.056 0.058 0.131 0.072 0.049
N 304 128 214 129 80 121 83 169 246 148 156 356 117 148 116 90 155 122 138 103
R1 R2 R3 R4 R5 R6 R7 R8 R9 R10 R11 R12 R13 R14 R15 R16 R17 R18 R19 R20
234
TABLE AIV-2.2 Location Choice Model – Independent Activity – 25 alternatives
LvoisLna 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
pAge - - - - - - - - - - - 0,1 2,4,3 - - - - - - -
VoSize 2 3-4 - - - - - - - - - - - - - - 0 1-4 - -
Vogord 0 0 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2
Urb 4 4 - - - 0,3,4 1,2 0,2,1 3,4 3,4 - - - - - - - - - -
Adur 2 2 0 1 2 4 - - - - - - - - - - - - 0 1
Comp - - - - - - - - - - - - - - 0,4 0,4 0,4 0,4 1,3 1,3
Aty - - 0,1,10,6 0,1,10,6 0,1,10,6 2 2 3 3 3 4,5 7 7 8,9 - - - - - -
SEC - - - - - - - - - - - - - - 0 1,2,3 - - - -
Day - - - - - - - - 0,2,5 1,6,3,4 - - - - 0,4,5,6 0,4,5,6 1,3,2 1,3,2 - -
Z1 0 0.006 0.03 0 0.021 0.006 0.036 0.024 0.022 0.065 0.056 0.055 0.043 0.044 0.024 0.033 0.038 0.007 0.015 0.019
Z2 0 0.006 0.023 0.087 0.026 0 0 0.087 0.011 0.013 0.014 0.039 0.085 0.082 0.012 0.057 0.025 0.035 0.015 0.028
Z3 0 0.006 0.075 0.013 0.041 0.006 0.048 0.016 0.011 0 0.021 0.07 0.017 0.088 0.012 0 0.038 0.035 0 0.028
Z4 0.022 0.025 0.068 0.013 0.015 0.035 0.048 0.071 0 0.091 0.049 0.109 0.077 0.088 0.048 0.033 0.013 0.014 0.007 0.019
Z5 0 0 0.008 0.013 0.031 0.006 0 0.031 0.055 0.026 0.007 0.039 0.06 0.038 0.06 0.016 0.038 0.035 0.052 0.009
Z6 0.011 0 0.023 0.1 0.052 0.029 0.012 0.031 0.077 0 0.028 0.062 0.009 0.033 0 0.033 0.038 0 0.037 0.028
Z7 0.045 0 0.113 0.1 0.046 0.012 0.012 0.134 0 0.026 0.035 0.016 0.103 0.06 0.024 0.09 0.038 0.056 0.067 0.046
Z8 0.067 0 0.105 0.087 0.057 0.035 0.12 0.031 0 0.078 0.028 0.047 0.017 0.121 0.083 0.041 0.127 0.084 0.007 0.056
Z9 0.011 0.051 0.015 0.013 0.036 0.012 0.12 0.055 0.022 0.013 0.104 0.008 0.068 0.049 0 0.057 0.013 0.014 0.075 0.019
Z10 0 0 0 0.05 0.046 0.029 0.096 0.055 0.066 0.091 0.062 0.039 0.068 0.06 0.202 0.025 0.013 0.049 0.075 0.056
Z11 0.034 0.013 0.053 0.025 0.021 0.069 0.024 0.031 0.055 0.078 0.097 0.008 0.026 0.044 0.012 0.049 0.063 0.049 0.082 0.046
Z12 0.011 0.013 0.068 0.05 0.046 0.064 0.024 0.102 0.088 0.039 0.014 0.016 0.009 0.022 0.012 0.016 0.025 0.063 0.03 0.056
Z13 0.101 0.032 0.09 0.037 0.031 0.104 0.072 0.016 0.033 0.091 0.056 0.039 0.009 0.016 0.06 0.025 0.063 0.056 0.037 0.046
Z14 0.056 0.045 0.023 0.025 0.041 0.023 0.024 0.024 0.022 0.052 0.042 0.039 0.017 0.022 0.024 0.057 0.025 0.098 0.015 0.056
Z15 0.022 0.019 0.045 0.037 0.036 0.052 0.06 0.031 0.121 0.013 0.062 0.023 0.043 0.016 0.012 0.016 0.063 0.182 0.127 0.037
R21 R22 R23 R24 R25 R26 R27 R28 R29 R30 R31 R32 R33 R34 R35 R36 R37 R38 R39 R40
235
TABLE AIV-2.2 (cont.)
LvoisLna 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
pAge - - - - - - - - - - - 0,1 2,4,3 - - - - - - -
VoSize 2 3-4 - - - - - - - - - - - - - - 0 1-4 - -
Vogord 0 0 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2
Urb 4 4 0,3,4 1,2 0,2,1 3,4 3,4 - - - - - - - - - -
Adur 2 2 0 1 2 4 - - - - - - - - - - - - 0 1
Comp - - - - - - - - - - - - - - 0,4 0,4 0,4 0,4 1,3 1,3
Aty - - 0,1,10,6 0,1,10,6 0,1,10,6 2 2 3 3 3 4,5 7 7 8,9 - - - - - -
SEC - - - - - - - - - - - - - - 0 1,2,3 - - - -
Day - - - - - - - - 0,2,5 1,6,3,4 - - - - 0,4,5,6 0,4,5,6 1,3,2 1,3,2 - -
Z16 0.101 0.045 0.015 0.037 0.057 0.081 0.012 0.016 0.055 0.039 0.035 0.039 0.026 0.016 0.036 0.025 0.025 0.014 0 0.019
Z17 0.034 0.102 0 0.025 0.026 0.017 0.012 0.016 0.033 0.052 0.007 0.008 0.034 0.005 0.083 0.049 0.038 0 0.067 0.074
Z18 0.09 0.038 0.03 0.087 0.031 0.046 0.024 0.016 0.033 0.013 0.056 0.031 0.06 0.038 0.071 0.049 0.013 0.042 0.06 0.046
Z19 0.056 0.057 0.015 0.037 0.036 0.029 0.024 0.016 0 0.013 0.021 0.047 0.026 0.038 0.024 0.049 0.038 0.014 0.052 0.074
Z20 0.056 0.045 0.03 0 0.077 0.017 0.036 0.031 0.022 0.013 0.028 0.07 0.051 0.005 0.06 0.049 0.076 0.049 0 0.056
Z21 0.045 0.121 0.038 0.087 0.021 0.069 0.084 0.071 0.099 0.026 0.056 0.023 0.026 0.011 0.012 0.041 0.038 0.021 0.09 0.019
Z22 0.045 0.096 0.038 0.037 0.036 0.11 0 0.047 0.033 0.026 0.014 0.031 0.068 0 0.06 0.049 0.038 0.021 0.022 0.037
Z23 0.079 0.115 0.038 0.025 0.077 0.081 0.048 0 0.077 0.026 0.014 0.016 0.051 0.011 0.012 0.057 0 0.014 0.007 0.028
Z24 0.056 0.083 0.023 0.013 0.041 0.035 0.012 0.39 0.011 0.078 0.035 0.07 0.009 0.049 0.036 0.025 0.063 0 0.037 0.037
Z25 0.056 0.083 0.038 0 0.052 0.035 0.048 0.008 0.055 0.039 0.062 0.055 0 0.038 0.024 0.057 0.051 0.049 0.022 0.065
N 89 157 133 80 194 173 83 127 91 77 144 128 117 182 84 122 79 143 134 108
R21 R22 R23 R24 R25 R26 R27 R28 R29 R30 R31 R32 R33 R34 R35 R36 R37 R38 R39 R40
236
TABLE AIV-2.3 Location Choice Model – Independent Activity – 25 alternatives
LvoisLna 1 1 1 1 1 1 1 1 1 1 1 1
VoSize - - - 0 1-4 - - 0 0 1-2 3-4 3-4
Vogord 2 2 2 3 3 3 3 4 4 4 4 4
Urb - 0,2 1,3,4 - - - - - - - - -
Comp 1,3 2 2 - - - - - - - - -
Wstat - - - 0,1 0,1 2 - - - - - -
SEC - - - 0,3,1 0,3,1 0,3,1 2 0,1 2,3 - - -
Ncar - - - - - - - - - - 0,2 1
Z1 0 0 0.042 0 0.009 0 0.013 0.011 0.025 0.02 0.014 0
Z2 0.005 0.022 0 0.026 0.022 0 0.007 0.011 0.017 0.045 0.007 0.024
Z3 0 0.022 0 0.009 0.013 0.028 0.007 0.032 0.059 0.03 0.021 0.006
Z4 0.016 0.055 0 0.026 0.058 0.014 0.034 0.147 0.017 0.025 0.048 0.018
Z5 0.016 0.022 0.014 0.017 0 0.007 0.007 0.063 0.034 0.02 0.048 0.024
Z6 0.01 0.022 0.021 0.026 0.004 0 0.02 0 0.008 0.03 0 0
Z7 0.021 0.022 0.099 0.078 0.04 0.028 0.027 0.032 0 0.03 0.007 0.012
Z8 0.026 0.11 0.014 0.052 0.018 0.014 0.04 0.021 0.068 0.045 0.007 0.024
Z9 0.031 0.033 0 0.034 0.04 0.014 0.034 0.011 0.034 0.061 0.028 0.029
Z10 0.031 0.055 0.042 0.026 0.085 0.07 0.114 0.105 0.042 0.035 0.117 0.071
Z11 0.057 0.11 0.063 0.052 0.036 0.021 0.034 0 0.034 0 0 0.041
Z12 0.031 0 0.035 0.069 0.054 0.042 0.04 0 0.025 0.035 0.034 0.029
Z13 0.047 0.011 0.063 0.026 0.071 0.049 0.02 0.042 0.102 0.066 0.014 0.018
Z14 0.031 0.099 0.113 0.034 0.058 0.028 0.067 0.042 0.051 0.04 0.021 0.029
Z15 0.078 0.022 0.106 0.095 0.134 0.162 0.067 0.137 0.051 0.086 0.124 0.059
R41 R42 R43 R44 R45 R46 R47 R48 R49 R50 R51 R52
237
TABLE AIV-2.3 (cont.)
LvoisLna 1 1 1 1 1 1 1 1 1 1 1 1
VoSize - - - 0 1-4 - - 0 0 1-2 3-4 3-4
Vogord 2 2 2 3 3 3 3 4 4 4 4 4
Urb - 0,2 1,3,4 - - - - - - - - -
Comp 1,3 2 2 - - - - - - - - -
Wstat - - - 0,1 0,1 2 - - - - - -
SEC - - - 0,3,1 0,3,1 0,3,1 2 0,1 2,3 - - -
Ncar - - - - - - - - - - 0,2 1
Z16 0.026 0.011 0.014 0.017 0.022 0.056 0.054 0.021 0.059 0.025 0 0.035
Z17 0.026 0.022 0 0 0.022 0.049 0.087 0.011 0.025 0.051 0.028 0.012
Z18 0.026 0.088 0.099 0.043 0.049 0.049 0.013 0.105 0.025 0.051 0.021 0.065
Z19 0.057 0.077 0.042 0.043 0.058 0.028 0.013 0.021 0.025 0.035 0.076 0.047
Z20 0.094 0.055 0.049 0.129 0.054 0.014 0.04 0.084 0.102 0.071 0.152 0.141
Z21 0.036 0.022 0 0.026 0.027 0.014 0.06 0.021 0.017 0.02 0.034 0.035
Z22 0.047 0 0.056 0.026 0 0.07 0.054 0.032 0.017 0.015 0.034 0.029
Z23 0.057 0.033 0.042 0.034 0.018 0.042 0.081 0.011 0.034 0.02 0.021 0.082
Z24 0.062 0.044 0.056 0.017 0.045 0.049 0.034 0 0.034 0.04 0.055 0.035
Z25 0.167 0.044 0.028 0.095 0.062 0.148 0.034 0.042 0.093 0.101 0.09 0.135
N 192 91 142 116 224 142 149 95 118 198 145 170
R41 R42 R43 R44 R45 R46 R47 R48 R49 R50 R51 R52
238
TABLE AIV-3.1 Location Choice Model – Joint Activity – 3 alternatives
Vohtt 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Dmax 0-1 0-1 0-1 0-1 2 2 2 2 3 3 3 3 3 3 3 3 3
NaH 0 1 1 1 0 1 1 1 - - - - - - - - -
Urb - 0,1,2,3 4 4 - - 0,2,1,3 4 - - - - - - - - -
VoSize - - 0-1 2-4 - 0 1-4 1-4 - - - - - - - - -
Aty - - - - - - - - 0,1,10,6,9 0,1,10,6,9 0,1,10,6,9 0,1,10,6,9 2,5,4,7,3 2,5,4,7,3 2,5,4,7,3 2,5,4,7,3 2,5,4,7,3
pAge - - - - - - - - 0,1,2 0,1,2 3,4 - - - - - -
Adur - - - - - - - - 0 1-2 1-2 - - - - - -
Wstat - - - - - - - - - 0 1,2 - - - - - -
NaSize - - - - - - - - - - - - 0-1 0-1 0-1 2-4 2-4
Vogord - - - - - - - - - - - - 0 1-4 1-4 - -
Driver - - - - - - - - - - - - - 0 1 - -
SEC - - - - - - - - - - - - - - - 0,1 2,3
same as previous 0.281 0.477 0.629 0.814 0.163 0.228 0.336 0.547 0.16 0.118 0.035 0.221 0.232 0.205 0.101 0.432 0.262
same as next 0 0 0 0 0.008 0 0 0 0 0 0 0 0 0 0 0 0
other 0.719 0.523 0.371 0.186 0.829 0.772 0.664 0.453 0.84 0.882 0.965 0.779 0.768 0.795 0.899 0.568 0.738
N 128 241 143 97 129 158 232 190 100 153 144 86 142 83 189 125 191
R1 R2 R3 R4 R5 R6 R7 R8 R9 R10 R11 R12 R13 R14 R15 R16 R17
239
TABLE AIV-3.2 Location Choice Model – Joint Activity – 3 alternatives
Vohtt 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Dmax 3 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
NaH - 0 1 1 - - - - - - - - - - - -
VoSize - - - - - - - - 0 1-4 - - - - 0-2 3-4
Aty 8 0,1,10,6,4,5,7 0,1,10,6,4,5,7 0,1,10,6,4,5,7 2,3 8,9 8,9 8,9 0,1,2,10,3,4,8 0,1,2,10,3,4,8 5,7,6,9 - - - - -
pAge - - - - - - - - - - - - 0,2 0,2 1 1
Adur - 0 0 0 0 0 0 0 1 1 1 1 2 2 2 2
Vogord - - 0 1-4 - - 0 1-4 - - - - - - - -
Abt - - - - - 0,5,4 1,2,3 1,2,3 - - - - - - - -
NaisA - - - - - - - - 0 0 0 1 - - - -
Tavail - - - - - - - - - - - - 0-2 3-4 - -
same as previous 0.458 0.065 0.315 0.16 0.284 0.771 0.641 0.458 0.119 0.315 0.083 0 0.039 0.128 0.006 0.048
same as next 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
other 0.542 0.935 0.685 0.84 0.716 0.229 0.359 0.542 0.881 0.685 0.917 1 0.961 0.872 0.994 0.952
N 168 108 124 187 95 118 103 107 118 143 385 82 412 117 346 124
R18 R19 R20 R21 R22 R23 R24 R25 R26 R27 R28 R29 R30 R31 R32 R33
240
TABLE AIV-3.3 Location Choice Model – Joint Activity – 3 alternatives
Vohtt 0 0 1-2 1-2 3 3 4 4
Dmax 4 4 - - - - - -
VoSize - - - - 0-1 3-4 - -
pAge 3,4 3,4 - - - - - -
Adur 2 2 - - - - - -
SEC 0,3,2 1 - - - - - -
Pwstat - - 0,1 2 - - - -
VoisA - - - - - - 0 1
same as previous 0.062 0.168 0.359 0.151 0.08 0.248 0.158 0.302
same as next 0 0 0.18 0.049 0.127 0.072 0.036 0.012
other 0.938 0.832 0.461 0.75 0.793 0.68 0.806 0.685
N 208 137 128 172 150 153 222 162
R34 R35 R36 R37 R38 R39 R40 R41
241
TABLE AIV-4.1 Location Choice Model – Joint Activity – 25 alternatives
Dmax 0 1 1 2 2 2 2 3 3 3 3 3 3 3 4 4 4 4 4 4
NaH -
0 1 0 1 1 -
0 1 1 1 1 1 1 - - - - - -
Dmin - - -
0 0 0 1-4 -
0 0 0 0 1-4 1-4 - -
0 1-4 - -
VoH - - - - -
0 1 - - - - - - - - - - - - -
NaSize - - - - - - - -
0 0 1-4 1-4 - - - - - - - -
Wstat - - - - - - - -
0 1,2 - - - - - - - - - -
Urb - - - - - - - - - -
0 1,4,2,3 - - - - - - - -
Vogord - - - - - - - - - - - -
0 1-4 - - - - - -
Adur - - - - - - - - - - - - - -
0 0 0 0 1 1
LvoisLNa - - - - - - - - - - - - - -
0 1 1 1 0 1
NaH - - - - - - - - - - - - - - -
0 1 1 - -
Comp - - - - - - - - - - - - - - - - - - -
0,1,2,3
Z1 0.116 0.047 0.078 0.025 0.082 0.028 0 0.008 0.027 0.036 0.031 0.03 0.009 0 0.074 0 0.022 0 0.067 0.003
Z2 0.107 0.047 0.064 0.038 0.071 0.103 0.016 0.017 0.036 0.095 0 0.05 0 0.005 0.032 0 0.035 0.006 0.027 0.016
Z3 0.19 0.056 0.054 0.063 0.002 0.084 0.005 0.017 0.116 0.119 0.02 0.026 0 0.005 0.063 0 0.044 0.006 0.027 0.013
Z4 0.116 0.028 0.118 0.051 0.102 0.093 0 0.017 0.116 0.077 0.041 0.017 0 0 0.053 0.008 0.053 0.006 0.027 0.006
Z5 0.091 0.047 0.049 0.025 0.133 0.028 0.005 0.017 0.036 0.012 0.01 0.04 0.005 0.011 0.032 0 0.026 0.006 0.027 0.013
Z6 0.033 0.056 0.093 0.063 0.051 0.056 0.098 0.013 0.04 0 0.031 0.03 0.024 0.016 0.021 0.008 0.044 0.034 0.04 0.006
Z7 0.033 0.028 0.088 0.063 0.071 0.073 0.017 0.042 0.036 0.024 0.031 0.063 0.047 0.054 0.063 0.008 0.04 0.023 0.013 0.006
Z8 0.041 0.075 0.103 0.038 0.041 0.103 0.083 0.029 0.036 0.024 0.051 0.059 0.033 0.054 0.021 0.008 0.026 0.034 0 0.016
Z9 0.05 0.075 0.074 0.051 0.061 0.033 0.057 0.029 0.071 0.012 0.041 0.033 0.047 0.022 0.032 0.008 0.018 0.017 0.027 0.016
Z10 0.05 0.028 0.069 0.013 0.041 0.023 0.062 0.021 0.009 0.048 0.132 0.053 0.005 0.07 0.053 0.008 0.009 0.023 0.067 0.009
Z11 0 0.019 0.034 0.038 0.071 0.061 0.078 0.034 0.027 0.06 0.082 0.059 0.081 0.076 0.053 0.016 0.022 0.051 0.067 0.034
Z12 0.008 0.056 0.02 0.013 0.02 0.061 0.093 0.042 0.045 0.071 0.01 0.083 0.052 0.086 0.011 0.023 0.044 0.017 0.053 0.016
Z13 0 0.065 0.015 0.076 0.031 0.033 0.093 0.05 0.098 0.012 0.031 0.066 0.066 0.081 0.042 0.023 0.022 0.04 0.013 0.006
Z14 0.017 0.037 0.025 0.025 0.061 0.07 0.062 0.055 0.027 0.119 0.061 0.04 0.085 0.076 0.032 0.023 0.009 0.051 0.04 0.022
Z15 0.025 0.028 0.044 0.063 0.031 0.042 0.041 0.029 0.045 0.071 0.112 0.059 0.047 0.135 0.063 0.023 0.048 0.023 0.04 0.022
R1 R2 R3 R4 R5 R6 R7 R8 R9 R10 R11 R12 R13 R14 R15 R16 R17 R18 R19 R20
242
TABLE AIV-4.1 (cont.)
Dmax 0 1 1 2 2 2 2 3 3 3 3 3 3 3 4 4 4 4 4 4
NaH -
0 1 0 1 1 -
0 1 1 1 1 1 1 - - - - - -
Dmin - - -
0 0 0 1-4 -
0 0 0 0 1-4 1-4 - -
0 1-4 - -
VoH - - - - -
0 1 - - - - - - - - - - - - -
NaSize - - - - - - - -
0 0 1-4 1-4 - - - - - - - -
Wstat - - - - - - - -
0 1,2 - - - - - - - - - -
Urb - - - - - - - - - -
0 1,4,2,3 - - - - - - - -
Vogord - - - - - - - - - - - -
0 1-4 - - - - - -
Adur - - - - - - - - - - - - - -
0 0 0 0 1 1
LvoisLNa - - - - - - - - - - - - - -
0 1 1 1 0 1
NaH - - - - - - - - - - - - - - -
0 1 1 - -
Comp - - - - - - - - - - - - - - - - - - -
0,1,2,3
Z16 0.008 0.009 0.005 0.038 0 0.009 0.057 0.059 0.054 0.036 0.041 0.04 0.09 0.054 0 0.062 0.048 0.08 0.08 0.066
Z17 0 0.056 0 0.013 0 0.014 0.026 0.063 0.045 0 0.02 0.063 0.081 0.076 0.042 0.093 0.04 0.074 0.067 0.05
Z18 0 0.037 0.005 0.038 0 0.014 0.031 0.059 0.062 0.036 0.01 0.053 0.09 0.022 0.053 0.07 0.048 0.04 0.053 0.082
Z19 0.017 0.009 0.01 0.025 0.02 0.005 0.01 0.042 0.009 0.06 0.061 0.063 0.076 0.054 0.032 0.054 0.048 0.068 0.04 0.066
Z20 0.017 0.056 0.029 0.025 0 0.033 0.041 0.055 0.018 0.036 0.122 0.063 0.062 0.054 0 0.039 0.07 0.074 0.013 0.088
Z21 0.017 0.028 0 0.063 0 0.005 0.016 0.055 0.009 0 0.01 0.007 0.028 0.011 0.021 0.101 0.088 0.051 0.04 0.094
Z22 0.008 0 0 0.051 0.01 0.009 0.016 0.076 0.018 0.024 0.01 0.017 0.019 0.005 0.011 0.101 0.026 0.04 0.053 0.1
Z23 0.041 0.037 0.005 0.038 0 0.014 0.016 0.055 0 0.024 0 0.007 0.019 0 0.095 0.109 0.066 0.085 0.053 0.082
Z24 0.017 0.047 0.01 0.025 0 0 0.016 0.067 0 0 0.02 0.003 0.005 0 0.042 0.109 0.075 0.091 0.04 0.094
Z25 0 0.047 0.01 0.038 0 0 0.021 0.067 0.018 0.012 0.02 0.007 0.028 0.027 0.063 0.109 0.026 0.062 0.027 0.075
N 121 107 204 79 98 214 193 238 112 84 98 303 211 185 95 129 227 176 75 319
R1 R2 R3 R4 R5 R6 R7 R8 R9 R10 R11 R12 R13 R14 R15 R16 R17 R18 R19 R20
243
TABLE AIV-4.2 Location Choice Model – Joint Activity – 25 alternatives
Dmax 4 4 4 4 4 4 4 4 4 4 4
Dmin - - - 0 1-4 - - - - - -
Wstat - - 0,2 0,2 0,2 1 - - - - -
Vogord 0-1 2-4 0 0 0 0 1-2 1-2 1-2 1-2 3-4
Adur 1 1 2 2 2 2 2 2 2 2 2
LvoisLNa 1 1 - - - - - - - - -
Comp 4 4 - - - - - - - - -
pAge - - 0,1 2,4,3 2,4,3 - - - - - -
Aty - - - - - - 0,1,10,8,7,4,5,9,6 0,1,10,8,7,4,5,9,6 0,1,10,8,7,4,5,9,6 2,3 -
SEC - - - - - - 0,2,3 0,2,3 1 - -
Abt - - - - - - 0,4,5 1,2,3 - - -
Z1 0.01 0.009 0.006 0.013 0 0 0 0 0 0.006 0.011
Z2 0.01 0 0 0 0 0.014 0 0 0 0.017 0.004
Z3 0 0.018 0.006 0 0 0.014 0 0.01 0 0 0
Z4 0.015 0.018 0.019 0 0 0 0 0 0.047 0.011 0.004
Z5 0 0 0 0 0 0 0.011 0.005 0 0.023 0.011
Z6 0.044 0.018 0 0.013 0.005 0 0 0 0.012 0.023 0.011
Z7 0.024 0.009 0.012 0.013 0.005 0.014 0 0.016 0 0.011 0.011
Z8 0.029 0.045 0 0 0.005 0.007 0.011 0 0.035 0.046 0.03
Z9 0.015 0.018 0 0.013 0.005 0 0 0.01 0.024 0.023 0.023
Z10 0.005 0.027 0 0 0 0 0 0.021 0.035 0.017 0.03
Z11 0.054 0.018 0 0.051 0.011 0.014 0 0.01 0.012 0.029 0.015
Z12 0.02 0.018 0 0 0 0.034 0 0.01 0.024 0.017 0.019
Z13 0.015 0.055 0 0.025 0 0.014 0 0.005 0.047 0.011 0.03
Z14 0.01 0.082 0.012 0 0.011 0.027 0.022 0 0 0.046 0.026
Z15 0.005 0.036 0.012 0.013 0 0.014 0.043 0.01 0 0.069 0.053
R21 R22 R23 R24 R25 R26 R27 R28 R29 R30 R31
244
TABLE AIV-4.2 (cont.)
Dmax 4 4 4 4 4 4 4 4 4 4 4
Dmin - - - 0 1-4 - - - - - -
Wstat - - 0,2 0,2 0,2 1 - - - - -
Vogord 0-1 2-4 0 0 0 0 1-2 1-2 1-2 1-2 3-4
Adur 1 1 2 2 2 2 2 2 2 2 2
LvoisLNa 1 1 - - - - - - - - -
Comp 4 4 - - - - - - - - -
pAge - - 0,1 2,4,3 2,4,3 - - - - - -
Aty - - - - - - 0,1,10,8,7,4,5,9,6 0,1,10,8,7,4,5,9,6 0,1,10,8,7,4,5,9,6 2,3 -
SEC - - - - - - 0,2,3 0,2,3 1 - -
Abt - - - - - - 0,4,5 1,2,3 - - -
Z16 0.073 0.073 0.049 0.051 0.063 0.021 0.108 0.052 0.059 0.023 0.04
Z17 0.102 0.091 0.019 0.038 0.1 0.041 0.043 0.078 0.012 0.017 0.026
Z18 0.059 0.055 0.049 0.063 0.053 0.021 0.022 0.016 0.024 0.051 0.034
Z19 0.029 0.055 0.031 0.063 0.042 0.068 0 0.031 0.035 0.04 0.064
Z20 0.034 0.109 0.091 0.076 0.074 0.027 0.065 0.021 0.059 0.04 0.14
Z21 0.151 0.055 0.111 0.127 0.153 0.144 0.032 0.14 0.094 0.08 0.068
Z22 0.024 0.055 0.074 0.203 0.074 0.137 0.172 0.135 0.106 0.04 0.064
Z23 0.098 0.036 0.123 0.089 0.121 0.144 0.075 0.119 0.071 0.086 0.087
Z24 0.059 0.055 0.216 0.101 0.105 0.082 0.086 0.176 0.145 0.128 0.087
Z25 0.117 0.045 0.241 0.051 0.174 0.164 0.312 0.135 0.165 0.154 0.155
N 205 110 162 79 190 146 93 193 85 175 265
R21 R22 R23 R24 R25 R26 R27 R28 R29 R30 R31
245
TABLE AV-1.2 Car Allocation to Non-Work Tour
distm 0 0 0 0 0 0 1 1 1 1 1 1 2 2 2 2 2 3-4
distf 0 0 0 0 0 1-4 0 1 1 1 1 2-4 - - - - - -
ntouf 0 0 0 0 1 - - - - - - - - - - - - -
nshim 0,2 0,2 0,2 1 - - - - - - - - - - - - - -
nNWm 1,4 1,4 2,3 - - - - - - - - - - - - - - -
nNWf 1,4 2,3 - - - - - - - - - - - - - - - -
TTcbF - - - - - - - 0-1 0-1 0-1 2-3 - - - - - - 0-1
AtourF - - - - - - - 2,10,4,3,6,5,7 2,10,4,3,6,5,7 8,9 - - - - - - - -
nNWf - - - - - - - 1 2,4,3 - - - - - - - - -
ntoum - - - - - - - - - - - - 0 0 0 0 1 -
Urban - - - - - - - - - - - - 0,1,2,3 0,1,2,3 4 4 - -
TrAcM - - - - - - - - - - - - 0 1 - - - -
RParkM - - - - - - - - - - - - - - 0 1-4 - -
AtourM - - - - - - - - - - - - - - - - - 2,3,4,5,8
nAcToF - - - - - - - - - - - - - - - - - 1,3
Male 0.348 0.333 0.27 0.514 0.074 0.257 0.37 0.731 0.555 0.425 0.492 0.365 0.553 0.823 0.784 0.691 0.364 0.743
Female 0.054 0.222 0.059 0.051 0.015 0.448 0.093 0.064 0.235 0.087 0.063 0.444 0.107 0.048 0.166 0.103 0.127 0.141
None 0.598 0.444 0.672 0.435 0.912 0.295 0.537 0.205 0.21 0.487 0.445 0.19 0.34 0.129 0.05 0.206 0.509 0.115
N 92 81 204 138 136 105 54 78 119 80 191 63 206 62 199 68 55 269
R1 R2 R3 R4 R5 R6 R7 R8 R9 R10 R11 R12 R13 R14 R15 R16 R17 R18
246
TABLE AV-1.2 Car Allocation to Non-Work Tour
distm 3-4 3-4 3-4 3-4 3-4 3-4 3-4 3-4 3-4 3-4
TTcbF 2-3 2-3 - - - - - - - -
AtourM 2,3,4,5,8 2,3,4,5,8 2,3,4,5,8 2,3,4,5,8 6,7 6,7 6,7 6,7 9,10 9,10
nAcToF 1,3 1,3 2 2 - - - - - -
nleim 0 1 0 1 - - - - - -
RParkM - - - - 0-1 0-1 0-1 2-4 - -
NWdurM - - - - 0-2 3 3 - - -
pAge - - - - - 0,2 1,3,4 - - -
BTM - - - - - - - - 0.1 2,3
Male 0.79 0.619 0.836 0.679 0.739 0.952 0.796 0.648 0.722 0.423
Female 0.065 0.048 0.147 0.198 0.188 0.024 0.185 0.185 0.069 0.115
None 0.145 0.333 0.017 0.123 0.073 0.024 0.019 0.167 0.208 0.462
N 62 63 116 81 165 84 108 54 72 52
R19 R20 R21 R22 R23 R24 R25 R26 R27 R28
247
AUTHOR INDEX
A
Aitken, 10
Anggraini, 95, 115, 154, 168
Antill, 9
Arentze, 3, 4, 27, 28, 44, 46-47, 58,
60-61, 63, 70,80-81, 94, 99-100,
110, 113, 124, 132-133, 142, 160,
178
Atkinson, 22
B
Ben-Akiva, 29
Benson, 70
Bhat, 3, 14, 24, 31, 44, 70, 94, 113
Bianco, 70
Boarnett, 10
Borgers, 10, 11, 16, 19, 21, 22, 44
Bowman, 29, 70
Bradley, 29, 113
Breiman, 79, 99
Brijst, 28
C
Cao, 12
Carrasco, 15, 69, 114
Chai, 12
Chandraskaran, 13, 132
Charypar, 27
Cotton, 9
Crane, 10
Curry, 18
D
Davis, H., 18
Davis, J., 10
Donnelly, 29, 30
Dijst, 27
Dowling, 9
Dueker, 10
E
Elliasberg, 18
Eluru, 31
Emery, 18
Ettema, 9, 10, 12
F
Fagnani, 9
Fosgerau, 29
Friedman, 79, 99
Frick, 27
Fujii, 3, 13, 23, 30, 70
Fujiwara, 21
G
Gliebe, 23, 30, 44, 94, 113, 132
Golob, 8, 10, 113, 132
Gordon, 9
Goulias, 13, 27, 47, 113, 132
Gronau, 8, 10
Gupta, 19
Guo, 3, 70
H
Habib, 114
Hagerstrand, 27
Hanson, S., 9, 10
Hanson, P., 9, 10
Harsanyi, 18
Hickman, 9
Hofman, 10, 11
Huber, 9
Hunt, 8, 69
248
I
Iovanna, 8
Isobe, 29
J
Janssenns, 28
Joh, 27
Johnston, 9
K
Kanaroglou, 23, 44, 94
Kawakami, 29
Kapoen, 27
Kandker, 47
Kass, 79, 99, 142, 160
Keeney, 18
Kikuchi, 30
Kim, 8
Kitamura, 3, 13, 30, 70
Knijn, 10
Kockelman, 2
Koppelmann, 16, 23, 29, 30, 44, 94,
113, 132
Kostyniuk, 13
Krishnamurti, 18
Kruchten, 25
Kumara, 23
Kwan, 9, 10, 27
L
Lawson, 8, 70
Lawton, 70
Lee, 9
Lemp, 2
Lenntrop, 27
Lillidahl, 9
Livne, 19
Lu, 92
Lula, 70
M
McNally, 1, 10, 113, 132
McWethy, 2
Meka, 23
Menasco, 18, 19
Messer, 18
Metcalf, 9
Miller, 15, 25, 44, 45, 46, 69, 70,
94, 114
Molin, 18
Morita, 14
Morris, 9, 10
Munsinger, 18
N
Nagel, 27
Nash, 19
Niemeier, 9, 113
Nurul, 46
O
Olshen, 79, 99
Oppewal, 18
Ortuzar, 1
P
Parkany, 92
Pas, 70
Pendhyala, 23, 30, 70, 113
Petersen, 8, 15, 26, 30, 69
Pinch, 9
Pinjari, 31
Ponje, 10
Pratt, 9
Presser, 9, 10
Pribyl, 27
Q
Quinlan, 79, 99
249
R
Redman, 10
Ren, 8
Rigeaux, 18
Roorda, 15, 25, 45, 46, 69, 70, 94
S
Schwanen, 9, 10
Scott, 23, 44, 94
Senbil, 13
Singell, 9
Sivakumar, 3, 31, 69
Spitze, 9
Srinivasan, 3, 14, 24, 31, 44, 69, 94
Steed, 113
Stone, 79, 99
Stopher, 9
Storey, 9
Strathman, 10
T
Timmermans, 3, 4, 7, 9-11, 16, 18, 19,
21-22, 27-28, 44, 46-47, 58, 60-61,
63, 70, 80-81, 94, 99-100, 110,
113, 124, 132, 133, 142, 154, 160,
168, 178
Turner, 9
V
Vadarevu, 9
Van ark, 19
Vance, 8
Van der lippe, 12
Van hoof, 28
Veldhuisen, 27, 28
Vidacovie, 27
Vovsha, 8, 15, 24, 25, 26, 29, 30, 69-
70, 113
W
Weber, 18
Wen, 16, 29
Wets, 28
White, 9
Willumsen, 1
Winkler, 18
Y
Yamamoto, 30
Z
Zhang, 16, 19, 21, 22, 44
250
SUBJECT INDEX
A
action variable, 79-82, 84-87, 94, 97,
99-101, 104, 107, 123-124, 138-
139, 142, 145-146, 148, 160, 163,
167
activity allocation, 4, 32, 51, 65, 95,
98, 108, 195
activity based, 2-4, 7, 26, 31-32, 44-
46, 48, 51, 65, 70, 93-94, 112, 114,
131-132, 155-156, 171, 190-191,
194-195
activity diary, 44, 47-48, 57, 60, 65,
68, 74, 94, 98, 117, 138, 153, 155,
activity level, 51, 60, 82, 84, 101, 103,
119, 121-123, 137, 139, 141, 147-
148, 150, 169, 173, 186,
activity generation, 2, 26, 31, 95, 189,
195
activity participation, 4, 5, 8, 10, 13,
15-17, 23, 28, 32, 34, 44-45, 48,
51, 65, 93-95, 97-98, 102-105,
107-110, 112-114, 129, 131-132,
189-192, 194, 196, 198
activity scheduling, 32, 44, 47-48, 52,
57, 60, 65, 68, 70, 76, 90, 93-95,
110, 113-115, 130-131, 153-154,
168-169
activity selection, 45, 59, 97, 103-104,
106, 109, 122, 191
activity travel pattern, 2-3, 5, 8, 21, 25,
27-28, 30-31, 45, 113, 171-172,
190-191, 194
activity travel behavior, 13-14, 24, 31,
48, 51, 74, 98, 117, 123, 138, 150,
155, 171, 191, 194-195, 197
activity type, 5, 14, 24, 26-28, 30, 48-
50, 71, 94-95, 97-98, 102-104, 108,
110, 112, 115, 117, 119, 121-122,
125-127, 133, 135, 138, 140, 146-
149, 151, 155-156, 173-174, 182,
184, 189, 191, 193, 196-198
agenda, 2, 3, 43, 113
albatross, 3,4,6, 32, 44-51, 58-59, 61-
62, 65-66, 70-71, 73, 80, 90, 94-95,
98-99, 109-110, 113-114, 117, 123,
127, 129, 131-133, 135-136, 138,
142, 149, 150, 153, 155-157, 169,
171-172, 177-179, 188, 191-196,
198
algorithm, 15, 27-28, 45, 79, 82, 90,
94, 99, 101, 109, 119, 123, 127,
139, 142, 150, 153, 160, 163, 168,
195
attribute, 2, 13, 15, 48-51, 68, 74, 80,
82, 87, 90, 93,98-99, 101, 117, 120,
122, 125, 131, 136-139, 141, 145-
148, 150, 153, 155, 162, 163, 166,
173, 176, 178-179, 187, 192, 194
B
behavior, 1-3, 8
bring/get, 11, 45, 49, 50, 71, 95, 96,
98, 102, 103, 104, 106, 107, 109,
110, 115, 117, 118, 119, 122, 127,
130, 142, 144, 145, 152, 162, 165,
175, 177, 183, 185, 187, 189, 192,
197
C
car allocation, 4, 8, 15, 31, 44, 51-52,
57, 59-60, 66, 68-71, 73-77, 79-80,
251
82-91, 95, 97, 115, 153-155, 157-
159, 161-163, 165-170, 191-192,
194-195
car deficient, 5, 8, 51-52, 60, 66, 68-
70, 88, 90, 153-155, 168-170, 195
car ownership, 8, 15, 50, 69, 118, 125,
132, 139-140, 146, 148, 186
chaid, 5, 28, 44, 60, 77, 79-80, 84, 90,
94, 98-99, 104, 107, 109, 123-124,
127, 131, 142, 145, 150, 153, 159-
160, 165, 168, 195
chi square, 64, 65, 79-81, 88, 99-100,
104, 107, 123, 142, 145-149, 159-
160, 166-167, 172-173, 176
choice facet, 2, 3, 7, 26
computational process model, 44, 71,
94, 142, 153,
condition variable, 45, 52, 68, 73, 79-
84, 87, 89-90, 94, 97-108, 110,
119-125, 127, 136-140, 142, 147,
149-150, 158, 160-163, 167-168,
196
confusion matrix, 86, 89
contingency coefficient, 64, 104, 107,
145-146, 148, 166-167
continuous choice, 60,-61, 63, 65, 112
continuous variable, 47, 58-59, 82,
101, 114, 141, 162
constraint, 2, 4, 12
constraint-based, 27
D
decision making, 1, 2, 4, 5, 7
decision rules, 4, 5, 44, 46-47, 77, 79,
84-85, 90, 94, 104, 107, 109, 114,
124, 125, 127, 136, 146-147, 149,
166, 169, 170, 172, 197
decision tree, 5, 28, 44-45, 47, 52, 57,
60-65, 68, 77, 79-80, 85, 90, 93-95,
97-99, 101-102, 104, 107, 110,
112, 115, 117, 119, 124-127, 132,
136, 143, 146-147, 149, 151, 154,
156, 160-161, 168-169, 172, 174,
192, 196- 198
departure time, 2, 59, 74, 113-114,
135
destination, 1, 2, 7, 8, 26, 59, 74
detour time, 5, 58, 131-133, 136, 196
dimension, 2, 62, 114, 132, 136-137,
189, 192
discrete choice, 2, 47, 60-61, 63-64
discretionary, 9-10, 14, 24, 48, 96,
115, 132, 167, 186-187, 194, 196
duration, 2, 5, 9-10, 12, 14, 23-24, 27-
28, 45, 51-52, 57-59, 61, 71, 74,
77-79, 82-83, 85, 89-90, 95, 97,
101-104, 107-108, 110, 112-115,
118-127, 135, 137-138, 141-142,
147-150, 152, 157, 159, 163-166,
169-170, 192, 194-195, 197-198
E
econometric, 3, 31, 46
episode, 2, 72
escorting, 25, 29, 46
F
forecasting, 1, 7, 113
four-step model, 1, 26, 46, 196
fixed activity, 95
flexible activity, 95
f-statistic, 65, 123, 124, 125, 127, 197
G
generation module, 51, 53, 57
goodness of fit, 21, 22, 63, 90, 93,
110, 146, 149, 151, 154, 169, 173,
189, 192, 195, 197-198
grocery shopping, 5, 16, 114, 191
252
H
hit ratio, 80, 85, 99, 104, 107, 146,
149, 168
household decision making, 3, 4, 5, 7,
8, 15, 23, 26-27, 32, 43-, 45, 46,
51, 57, 65, 96, 113, 132-133, 154,
171, 178, 191-192, 194-195
hold out set, 94
household activity, 3, 27, 28, 51, 106,
109, 110, 140, 155, 195
household member, 3, 4, 8, 13-19, 21-
25, 28-30, 43, 45, 47-48, 51, 59,
69, 74, 98, 112, 114-115, 117, 130,
133, 135, 139, 156, 159, 171, 191-
192, 194, 197
household task, 4, 5, 9, 10, 12, 17, 21,
43, 48, 51-52, 59, 79, 93-95, 97,
102-104, 107-110, 114-115, 134,
156, 186, 196
household level, 7, 30, 31, 46, 51, 66
human decision making, 1, 4, 52
I
impact table, 77, 80, 87, 89-90, 94, 99,
104, 107, 110, 124-125, 142, 146-
149, 153, 155, 160, 166-167, 169,
198
independent activity, 17, 53, 58, 60
individual activity, 8, 27, 30, 194
individual decision making, 113, 154,
190
individual travel pattern, 3, 4
individual level, 14, 30-31, 46, 57, 82,
101, 121, 151, 191
induction method, 61, 69
integration, 6, 53, 57
interdependency, 2-3, 15, 31, 46, 58,
134, 190-191
institutional constraint, 2
intra household interaction, 29, 30
J
joint activity, 4, 5, 13-16, 23-25, 28-
30, 32, 44-45, 48, 51-52, 57, 59,
65, 93, 96, 112-115, 117, 119,
121-122, 124-127, 131-132, 136-
138, 140-141, 143-146, 148-151,
158, 189-192, 194, 196, 198
joint participation, 13, 14, 29-30, 52,
60, 93-96, 112, 114, 127, 129, 134,
141, 156, 193, 195-196
joint decision making, 11, 32, 50-51,
57, 59, 66, 94-95, 129
L
land use, 68, 82, 121, 132, 138, 163
leaf node, 61-65, 80, 84-85, 99, 104,
107, 124, 145, 148, 165-166
leisure, 5, 10-16, 23, 49-50, 71, 96,
102, 104, 113-115, 117-119, 125-
126, 133-134, 141, 143-144, 151,
156-157, 161, 164-165, 167, 169,
174, 182, 184, 186-187, 196-197
likelihood, 10, 63-64, 84, 99
location choice, 5, 30, 52, 58-59, 68-
69, 77, 79-80, 90, 131-133, 143,
148-150, 153, 156, 168, 195-196
long term, 2, 8
M
maintenance activity, 10, 12-13, 16,
23, 29-30, 39, 43, 70, 113, 133
mandatory, 49, 58, 72
microsimulation, 3
mode choice, 67
mon data, 4, 49, 66, 74-75, 78, 99
monotonicity, 87-88, 101
multinomial logit, 11, 15
253
N
non monotonous, 81, 88, 89, 100
non-work activity, 5, 8, 23-24, 49, 52,
57-59, 103, 115, 121-122, 125,
135, 137, 156, 158-159, 162-164,
170, 176
non-work tour, 5, 52, 57, 59-60, 66,
73, 97, 154-157, 159-160, 163-170,
192-193, 196, 198
non-household task, 5, 51, 95
null model, 65, 85
O
observation, 66
operational, 2, 52, 95
origin, 59, 74
P
paradigm, 1
pattern, 58,
person level, 51, 53, 66
postcode, 58, 82, 88, 132, 135-137,
139, 147, 150, 164, 179, 188
prediction, 45-46, 50, 57, 60-61, 64-
65, 80, 86, 99, 107, 110, 124-125,
127, 139, 143, 151, 173-174, 177-
178, 180, 188-190, 193, 197
probability, 8, 10, 12, 27-28, 30, 45,
61-63, 68, 85-86, 88-90, 97, 104,
106-109, 144-145, 154, 156, 168-
170, 174, 198
probabilistic theta, 64
probabilistic assignment, 80, 87
R
route choice, 1, 2
road capacity, 1
rule-based, 3, 5, 15, 27, 44-45, 47, 80,
94, 100, 109, 123-124, 127, 143,
151, 161, 196
resource allocation, 3, 4, 7, 27, 32, 44,
48, 59, 65, 136, 191-192, 195
S
schedule level, 51, 103, 121, 138, 140,
142, 161, 174, 177
sequential, 5, 6, 52,
service, 11-12, 49-50, 91, 95-96, 98,
102, 104, 107-109, 115, 117-118,
133-134, 141, 143-144, 156-157,
161, 164, 174, 182, 184, 186, 196-
197
shared activity, 20, 29
shopping, 5, 11-14, 16-17, 49-50, 71,
95-96, 98, 102, 104, 107-109, 113-
115, 117-119, 126, 134, 136-137,
141, 143-144, 150-151, 156-159,
161, 164-165, 167, 169, 174, 182,
184, 186, 190, 196-197
short-term, 8
skeleton, 28, 46, 47, 51
simulation model, 27
socio-demographic, 51
socio-economic, 1, 13, 69, 79, 82
social activity, 5, 51, 97
situational constraint, 2
space-time constraint, 2, 27-28, 45, 58,
133-134
space-time prism, 58, 134, 136, 141
spatial, 1, 10, 28, 47, 68, 110, 125,
139
split criterion, 64, 65, 123, 143
standard deviation, 66
start time, 28, 45, 52, 57, 58, 59, 61,
71, 97, 98, 112, 113, 114, 115, 117,
119, 122-127, 130, 135, 139, 141-
142, 156-157, 162-163, 169-170,
183, 188, 190, 192, 197-198
T
254
task allocation, 3-5, 8, 10-12, 16,
27, 30-31, 44, 48, 59, 79, 93-94,
97, 102-104, 107, 109-110, 134,
156, 189-190, 192, 194-196, 198
time allocation, 8, 10, 15
time of day, 4, 8, 51, 173-174, 176,
184, 187-188, 198
time use, 3, 16, 20-21, 23, 138
timing, 2, 46, 77, 96
tour-based, 2, 158
tour level, 154, 161, 164, 168, 169,
174, 177, 198
touring, 49-50, 72, 96, 102, 104, 115,
117-118, 125-126, 133-134, 137,
143-144, 151, 156-157, 161, 164,
167, 169, 186, 196-197
trade off, 70
traditional, 1, 2
traffic flow, 1
training set, 61, 63, 85-86, 89, 99, 124,
142, 145, 146, 166-167
transport demand, 1-4, 7, 32, 65, 70,
154, 191-192, 194
transport mode, 1-2, 7-8, 16, 26, 44-
45, 51-52, 57-60, 66, 69, 71, 73-74,
90, 94-98, 103, 117, 120, 122, 134,
138, 154-156, 165, 168-169, 176-
177, 187-189, 191, 195
travel arrangement, 15, 24, 25, 69
travel demand, 1, 3, 5, 7, 14, 32, 51,
65, 69, 93, 114, 132-133, 179,
192-193
travel behavior, 1, 13-14, 24, 31, 48,
51, 98, 113, 133, 139. 154, 156
travel mode, 2, 15, 16, 29, 59, 135
trip-based, 1, 2, 70, 158
trip-chaining, 5, 8, 9, 60, 74
trip-generation, 1, 31
travel pattern, 2, 3, 4, 14, 114, 1
U
urban planning, 1
unit of analysis, 2
utility function, 2, 15, 17-23
utility maximization, 23, 20, 27, 29,
45, 95
V
validation set, 87, 90, 99
variable, 10-16, 20-21, 23-25, 30-31,
45-47, 50, 52, 58-59, 61, 63, 68-71,
73, 79-85, 87-90, 93-94, 97, 99-
105, 108-110, 117, 119-125, 132,
137-143, 147-150, 154, 156, 159,
161-164, 167-170, 173-175, 177,
180, 183, 187-189, 197
W
work activity, 13, 23-24, 51-52, 57-60,
70-71, 73-74, 77-79, 82, 84-86, 88,
90-91, 95-96, 101, 103, 106, 110,
120-122, 127, 134-135, 155, 159,
163-164, 177, 190, 196, 198
work related, 52, 53, 59
work tour, 5, 59
255
List of Publications
International Scientific Journal
Anggraini, R., Arentze. T.A., Timmermans, H.J.P. (2008). Car Allocation between
Household Heads in Car Deficient Households: A Decision Model. European Journal of Transportation and Infrastructure Research, 8(4), pp. 301-319.
Anggraini, R., Arentze. T.A., Timmermans, H.J.P. (2009). Continuous Choice Model
of Timing and Duration of Joint Activities. Transportation Research Record: Journal of the Transportation Research Board, No. 2135: Travel Behavior 2009, Volume 2, pp.
Anggraini, R., Arentze. T.A., Timmermans, H.J.P. (2010). Car Allocation Decisions in
Car-Deficient Households: The Case of Non-Work Tours. Transportmetrica Journal (forthcoming).
Conference Proceedings
Anggraini, R., Arentze. T.A., Timmermans, H.J.P. (2006). A Model of Within-
Household Travel Activity Decisions Capturing Interactions Between Household
Heads. In J.P van Leeuwen and H.J.P. Timmermans (eds). Progress in Design and Decision Support Systems in Architecture and Urban Planning , Eindhoven
University of Technology, The Netherlands, pp. 19-33.
Anggraini, R., Arentze. T.A., Timmermans, H.J.P. (2007). Refining Albatross:
Modeling Household Activity Generation and Allocation Decisions Using
Decision Tree induction. In: Proceeding of the 11th WCTR Conference, UC
Berkeley, USA 2007. (CD-ROM, pp. 21).
Anggraini, R., Arentze. T.A., Timmermans, H.J.P. (2007). Modeling Car Allocation
Decisions in Automobile Deficient Households. In: Proceeding ETC 2007 Conference, Noordwijk, The Netherlands. (CD-ROM, pp. 22).
Anggraini, R., Arentze. T.A., Timmermans, H.J.P. (2008). Using the Activity-based
ALBATROSS Model to Support Transport Planning in Indonesia. In: Proceeding ISSM 2008, Delft, The Netherlands. (CD-ROM, pp. 85-91).
256
Anggraini, R., Arentze. T.A., Timmermans, H.J.P. (2008). Modeling Joint Activity
Participation and Household Task Allocation. In: Proceeding AATT 2008 Conference, Greece, Athens. (CD-ROM, pp. 15)
Anggraini, R., Arentze. T.A., Timmermans, H.J.P. (2009), Gender Roles and Activity-
Travel Patterns. In: Proceedings Household Activity-Travel Behavior Analysis for Urban Policy-Making, JSPS-NOW Workshop, Eindhoven, The Netherland. (On-
line, pp. 31)
Anggraini, R., Arentze. T.A., Timmermans, H.J.P. (2009). Household Location Choice
Models for Independent and Joint Non-Work Activities. In: Proceedings XIII Euro Working Group on Transportation Meeting, Padua, Italy. (CD-ROM, pp. 9)
Anggraini, R., Arentze. T.A., Timmermans, H.J.P., and Feng, T. (2009). Modeling
Household Activity Participation in a Rule-based System of Travel Demand:
Decision of Two Household Heads. In: Proceedings EASTS 09, Surabaya,
Indonesia (On-line, pp. 15)
257
CURRICULUM VITAE
Renni Anggraini was born in Banda Aceh, Indonesia, in September 23, 1971. She
graduated from the Syiah Kuala University, Banda Aceh as a Civil Engineer in 1996.
Soon after the graduation, she became an academic staff in the Civil Engineering
Department of Faculty of Engineering at the same university in 1997. In accordance
with her research interest, she was appointed at the Transportation group. A year later,
she was awarded a competitive fellowship for post-graduate studies under Japanese
government scholarship, namely MONBUSHO. With this award, she further continued
her study at the Department of Civil and Environmental Systems Engineering at
Nagaoka University of Technology, Japan. She graduated as a Master of Engineering
in 2001. During these two years, her major research project concerned about the
application of discrete choice models to simulate individual activity-travel behavior in
Nagaoka city, Japan.
From 2005 – 2009 she was a PhD student at Urban Planning Group at Eindhoven
University of Technology, the Netherlands. Her research still focused on activity-travel
behavior, expanding the individual decision making to household decision making. She
was also exploring an alternative modeling approaches, especially those established on
rule-based models or computer process models.
Her current research interests are in the areas of urban and transport planning, activity-
travel behavior, transport demand management, transport in developing countries, and
various other domains.
Shortly after completing her PhD study, she will be homecoming to the Syiah Kuala
University in Banda Aceh to pursue her career as an academic staff in the Department
of Civil Engineering.
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nr 8Informatievoorziening en beheerprocessenir A. Nauta / drs J. Smeets (red.)Prof. H. Fassbinder (projectleider)ir A. Proveniers, drs J.v.d. Moosdijk
nr.9Strukturering en verwerking van tijdgegevens voor de uitvoering van bouwwerkenir W.F. Schaeferir P.A. Erkelens
nr 10Stedebouw en de vorming van een speciale wetenschapK. Doevendans
nr 11Informatica en ondersteuning van ruimtelijke besluitvormingdr G.G. van der Meulen
nr 12Staal in de woningbouw, korrosie-bescherming van de begane grondvloerir E.J.F. Delsing
nr 13Een thermisch model voor de berekening van staalplaatbeton- vloeren onder brandomstandighedenir A.F. Hamerlinck
nr 14De wijkgedachte in NederlandGemeenschapsstreven in een stedebouwkundige contextdr ir K. Doevendansdr R. Stolzenburg
nr 15Diaphragm effect of trapezoidally profiled steel sheets. Experimental research into the influence of force applicationir A.W.A.M.W. v.d. Bogaard
nr 16Versterken met spuit-ferrocement.Het mechanische gedrag van met spuit-ferrocement versterkte gewapende betonbalkenir K.B. Lubir M.C.G. van Wanroy
nr 17De tractaten van Jean Nicolas Louis Durandir G. van Zeyl
nr 18Wonen onder een plat dak.Drie opstellen over enkele vooronder-stellingen van de stedebouwdr ir K. Doevendans
nr 19Supporting decision making processesA graphical and interactive analysis of multivariate datadrs W. Adams
nr 20Self-help building productivityA method for improving house building by low-income groups applied to Kenya 1990-2000ir P. A. Erkelens
nr 21De verdeling van woningen: een kwestie van onderhandelendrs V. Smit
nr 22Flexibiliteit en kosten in het ontwerp- proces Een besluitvormingonder-steunend modelir M. Prins
nr 23Spontane nederzettingen begeleidVoorwaarden en criteria in Sri Lankair P.H. Thung
nr 24Fundamentals of the design of bamboo structuresO. Arce-Villalobos
nr 25Concepten van de bouwkundeProf. dr ir M.F.Th. Bax (red.) dr ir H.M.G.J. Trum (red.)
nr 26Meaning of the siteXiaodong Li
nr 27Het woonmilieu op begrip gebrachtJaap Ketelaar
nr 28Urban environment in developing countrieseditors: dr ir Peter A. Erkelens dr George G. van der Meulen
nr 29Stategische plannen voor de stadOnderzoek en planning in drie stedenProf. dr H. Fassbinder (red.)ir H. Rikhof (red.)
nr 30Stedebouwkunde en stadsbestuurir Piet Beekman
nr 31De architectuur van DjennéEen onderzoek naar de historische stad P.C.M. Maas
nr 32Conjoint experiments and retail planningHarmen Oppewal
nr 33Strukturformen Indonesischer Bautechnik Entwicklung methodischer Grundlagen für eine 'konstruktive pattern language' in IndonesienHeinz Frick
nr 34Styles of architectural designingEmpirical research on working styles and personality dispositionsAnton P.M. van Bakel
nr 35Conjoint choice models for urban tourism planning and marketingBenedict Dellaert
nr 36Stedelijke Planvorming als co-produktieProf. dr H. Fassbinder (red.)
nr 37 Design Research in the Netherlandseditors: Prof. dr R.M.Oxman, Prof. dr ir. M.F.Th. Bax,Ir H.H. Achten
nr 38 Communication in the Building IndustryBauke de Vries
nr 39 Optimaal dimensioneren van gelaste plaatliggers
nr 40 Huisvesting en overwinning van armoededr.ir. P.H. Thung en dr.ir. P. Beekman (red.)
nr 41 Urban Habitat: The environmentof tomorrowGeorge G. van der Meulen, Peter A. Erkelens
nr 42A typology of jointsJohn C.M. Olie
nr 43Modeling constraints-based choices for leisure mobility planningMarcus P. Stemerding
nr 44Activity-based travel demand modelingD. Ettema
nr 45Wind-induced pressure fluctuations on building facadesChris Geurts
nr 46Generic RepresentationsHenri Achten
nr 47Johann Santini AichelDirk De Meyer
nr 48Concrete behaviour in multiaxialcompressionErik van Geel
nr 49Modelling site selectionFrank Witlox
nr 50Ecolemma modelFerdinand Beetstra
nr 51Conjoint approaches to developing activity-based modelsDonggen Wang
nr 52On the effectiveness of ventilationAd Roos
nr 53Conjoint modeling approaches for residential group preverencesEric Molin
nr 54Modelling architectural design information by featuresJos van Leeuwen
nr 55A spatial decision support system forthe planning of retail and servicefacilitiesTheo Arentze
nr 56Integrated lighting system assistantEllie de Groot
nr 57Ontwerpend leren, leren ontwerpendr.ir. J.T. Boekholt
nr 58Temporal aspects of theme park choice behavoirAstrid Kemperman
nr 59Ontwerp van een geïndustrialiseerde funderingswijzeFaas Moonen
nr 60Merlin: A decision support system foroutdoor leisure planningManon van Middelkoop
nr 61The aura of modernityJos Bosman (nog niet gepubliceerd)
nr 62Urban Form and Activity-Travel PatternsDaniëlle Snellen
nr 63Design Research in the Netherlands 2000Henri Achten
nr 64Computer Aided DimensionalControl in Building ConstructionRui Wu
nr 65Beyond Sustainable Buildingeditors: Peter A. Erkelens Sander de Jonge August A.M. van Vlietco-editor: Ruth J.G. Verhagen
nr 66Das globalrecyclingfähige HausHans Löfflad
nr 67Cool Schools For Hot SuburbsRené J. Dierkx
nr 68A Bamboo Building Design Decision Support ToolFitri Mardjono
nr 69Driving rain on building envelopesFabien van Mook
nr 70Heating Monumental ChurchesHenk Schellen
nr 71Van Woningverhuurder naar Aanbieder van WoongenotPatrick Dogge
nr 72Moisture transfer properties of coated gypsumEmile Goossens
nr 73Plybamboo Wall-panels for HousingGuillermo E. González-Beltrán
nr 74The Future Site-ProceedingsGer MaasFrans van Gassel
nr 75Radon transport in Autoclaved Aerated ConcreteMichel van der Pal
nr 76The Reliability and Validity of Interactive Virtual Reality Computer ExperimentsAmy Tan
nr 77Measuring Housing Preferences Using Virtual Reality And Belief NetworksMaciej A. Orzechowski
nr 78Computational Representations of Words and Associations in Architectural DesignNicole Segers
nr 79Measuring and Predicting Adaptationin Multidimensional Activity-Travel PatternsChang-Hyeon Joh
nr 80Strategic BriefingFayez Al Hassan
nr 81Well Being in HospitalsSimona Di Cicco
nr 82Solares BauenImplementierungs- und Umsetzungs-aspekte in der Hochschulausbildung in ÖsterreichGerhard Schuster
nr 83Supporting Strategic Design of workplace Environments with Case-Based Reasoning Shauna Mallory-Hill
nr 84ACCEL: a Tool for Supporting Concept Generation in the Early Design PhaseMaxim IvashkovMaxim Ivashkovnr 85Brick-mortar interaction in masonry under compressionAd Vermeltfoort
nr 86 Zelfredzaam WonenGuus van Vliet
nr 87Een ensemble met grootstedelijke allureJos Bosman/Hans Schippers
nr 88On the Computation of Well-Structured Graphic Representations inArchitectural Design Henri Achten
nr 89De Evolutie van een West-Afrikaanse Vernaculaire ArchitectuurWolf Schijns
nr 90ROMBO tactiekChristoph Maria Ravesloot
nr 91External coupling between building energy simulation and computational fluid dynamicsEry Djunaedy
nr 92Design Research in theNetherlands 2005Editors:Henri AchtenKees DorstPieter Jan StappersBauke de Vries
nr 93Ein Modell zur baulichen TransformationJalil H.Saber Zaimian
nr 94Human Lighting DemandsHealthy Lighting in an Office EnvironmentMyriam Aries
nr 95A Spatial Decision Support System for the Provision and Monitoring of Urban GreenspaceClaudia Pelizaro
nr 96Leren CreërenAdri Proveniers
nr 97SimlandscapeRob de Waard
nr 98Design Team CommunicationAd den Otter
nr 99Humaan-EcologischGeoriënteerde WoningbouwJuri Czabanowski
nr 100HambaseMartin de Wit
nr 101Sound Transmission through Pipe Systems and into Building StructuresSusanne Bron - van der Jagt
nr 102Het Bouwkundig ContrapuntJan Francis Boelen
nr 103A Framework for a Multi-Agent Planning Support SystemDick Saarloos
nr 104Bracing Steel Frames with Calcium Silicate Element WallsBright Mweene Ng'andu
nr 105Naar een nieuwe houtskeletbouwF.N.G. De Medts
nr 106Anatomy of DwellingEnno Wiersma(nog niet gepubliceerd)
nr 107Healing ArchitectureEwa Mosiniak(nog niet gepubliceerd)
nr 108Geborgenheiddrs T.E.L. van Pinxteren(nog niet gepubliceerd)
nr 109Modelling Strategic Behaviourin Anticipation of CongestionQi Han
nr 110Reflecties op het WoondomeinFred Sanders
nr 111On Assessment of Wind Comfort by Sand ErosionGabor Dezso
nr 112Bench Heating in Monumental Churches Dionne Limpens-Neilen
nr 113RE. ArchitectureAna Pereira Roders
nr 114Toward Applicable Green ArchitectureUsama El Fiky
nr 115Knowledge Representation UnderInherent Uncertainty in a Multi-AgentSystem for Land Use PlanningLiying Ma
nr 116Integrated Heat Air and MoistureModeling and SimulationJos van Schijndel
nr 117Concrete behaviour in multiaxial compressionJ.P.W. Bongers
nr 118The Image of the Urban LandscapeAna Moya Pellitero
nr 119The Self-Organizing City in VietnamStephanie Geertman
nr 120A Multi-Agent Planning Support System for Assessing Externalities of Urban Form ScenariosRachel Katoshevski-Cavari
nr 121Den Schulbau neu denken, fühlen und wollenUrs Christian Maurer-Dietrich
nr 122Peter Eisenman Theories and PracticesBernhard Kormoss
nr 123User Simulation of Space UtilisationVincent Tabak
nr 124Moisture Transport in Cavity Brick WallA. Aghai(nog niet gepubliceerd)
nr 125In Search of a Complex System ModelOswald Devisch
nr 126Lighting in Work Environment direct Effects of Lighting Level and Spectrum on Pshychophysiological Variables Grazyna Goricka
nr 127Flanking Sound Transmission trough Lightweight Framed Double Leaf WallsStefan Schoenwald
nr 128Bounded Rationality and Spatial- Temporal Pedestrian Shopping BehaviourWei Zhu
nr 129Travel information Impact on activity travel patternZhongwei(nog niet gepubliceerd)
nr 131Allemaal WinnenM.J. Bakker(nog niet gepubliceerd)
nr 132Architectural Cue Model in EvacuationSimulation for Underground Space DesignChengyu Sun
nr 133Uncertainty and sensitivity analysis in building performance simulation fordecision support and design optimizationChristina Hopfe
nr 134Facilitating distributed collaboration in the AEC/FM sector using Semantic Web TechnologiesJakob Beetz
nr 135Circumferentially Adhesive Bonded Glass Panes for Bracing Steel Frame in FaçadesEdwin Huveners
nr 136Circumferentially Adhesive Bonded Glass Panes for Bracing Steel Frame in FaçadesEdwin Huveners
nr 137Nog niet bekend Mariette van Stralen(nog niet gepubliceerd)
nr 138Nog niet bekendJos Smeets(nog niet gepubliceerd)
nr 139Lateral behavior of steel frames with discretely connected precast concrete infill panelsPaul Teeuwen(nog niet gepubliceerd)
nr 140Nog niet bekendPerica Savanovic(nog niet gepubliceerd)
nr 130Co-simulation for performence prediction of innovative integrated mechanical energy systems in buildingsMarija Trcka