Introduction to Activity-Based Modeling

Joshua AuldTransportation Research and Analysis Computing CenterArgonne National Laboratory

November 28, 2011

Travel Demand ModelingCurrent and Future Applications

Roadway planning / new construction– Decreasing funding for new roads

Environmental/Air quality impacts– More important after ISTEA, SAFETEA-LU– Conformity analysis

Travel Demand Management– Large collection of strategies– Increase efficiency of system (TSM)– Change user behavior (TDM)

• Congestion pricing, ride-share program, etc.– ITS / Operations

Activity based model theory

Travel is derived from participation in activities– Not accounted for in 4-step models

Time and space between the activities generates travel

Activity participation is modeled at household/individual level– Microsimulation model

Individual’s activity participation constrained by:– Time availability– Location– Institutional characteristics (operating hours, etc.)– Household considerations

Activity patterns are generated by individuals which satisfy these constraints and meet some other criteria

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Activity based model theory (cont.)

Figure shows a daily activity pattern graph (for travel in 1-dimension)– Vertical lines represent activities– Diagonal lines are travel episodes

Explicit representation of time of occurrence for all travel episodes, linked to associated activities

ABM generates an activity pattern for modeled individuals– Know when and where they are

traveling at all times12:00 AM

2:00 AM

4:00 AM

6:00 AM

8:00 AM

10:00 AM

12:00 PM

2:00 PM

4:00 PM

6:00 PM

8:00 PM

10:00 PM

12:00 AM

0 1 2 3 4 5 6Location

Tim

e

Shopping

Work

Home

Lunch

Home

Activity based model theory (cont.)

It is the goal of an activity based model to develop an activity schedule that:– Satisfies all of the given constraints, and– Satisfies some schedule optimization criteria

The models take as inputs:– Individual/household attributes– Environment attributes (land-use, activity locations, transportation networks, etc.)– Attributes and actions of other individuals

Then use these inputs to model a series of choices:– What activities to schedule– When to schedule them– Where to schedule them– Who to go with– How to get there– How long to stay

How these choices are modeled depends on the type of model used

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Approaches to Activity Based Modeling

Two general approaches to modeling activity participation choices– Econometric models– Rule-based models

In econometric type models:– Models are usually tour based – select activity tours from predefined set– Utility maximization to model pattern formation

In rule-based models:– Computational process models or other derived decision rules– Bottom-up approach to schedule building

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Econometric models

Decisions modeled using discrete choice– Usually nested logit

models

Model sequence:– Number and type of

tours -> Stops in each tour -> mode choice for tour, etc.

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Example of a nested logit model for decision making

Source: Wen and Koppelman (2000)

Econometric models (cont.)

8Source: Wen and Koppelman (2000)

Econometric models are generally tour-based– Model the number and purpose of activity tours– Chose from a set of pre-defined tours

Examples:– Bowman-Ben Akiva (1996) and derivatives (DAYSIM)– PB Consult models (MORPC, NYMTC, etc.)– Jakarta Model - Yagi and Mohammadian (2008)– Wen and Koppelman (2000)

Criticisms of econometric models

Unrealistic behavioral assumptions– Utility maximization in decision making by individuals

Artificially restrict activity scheduling to predefined choices– Can not represent full range of scheduling behavior– Reduces number of choices to be modeled, i.e. combinatorial problem– No consideration of dynamics (full day selected at one time)

Limitations on time-scale of analysis– Usually discretized to time-of-day periods

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Rule based model overview

Create activity schedules based on heuristics– Computational Process Models or other derived rules

Attempt to model the underlying process of activity scheduling

Examples:– SCHEDULER (Garling 1989)– AMOS (Pendyala 1995)– ALBATROSS (Arentze, Timmermans 2000)– TASHSA (Roorda, Doherty, Miller 2005)– ADAPTS (Auld and Mohammadian 2009)

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Computational process models

Production system (Newell, Simon 1972) for modeling decision making behavior– Rules as condition-action pairs– Describe how information relating to a choice is searched – Choice made depends on current information acting on set of rules– Allows incorporation of learning– Representation of process of decision making

Production systems often modeled as– Decision trees (ALBATROSS)– Heurstic rules (SCHEDULER, TASHA)

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Data requirements

Rule-based activity scheduling models have extensive data needs:

– Synthesized population• Socio-demographic characteristics of individuals

– Synthesized city/environment• Activity locations/operating hours• Roadway and transit networks• Land use variables

– Activity diary/scheduling data• Activity participation/generation rates• Conflict resolution rules• Planning process data• Executed schedules for model validation

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ALBATROSS model overviewArentze and Timmermans (2000).

Rule-based model for predicting:– Activity participation, location, timing, duration, party and travel mode choice– Includes household, institutional and spatio-temporal constraints

Decision rules modeled using CHAID decision trees at each step– Derived from activity survey data

Model attempts to simulate daily activity schedule creation for individuals– Long-term decisions considered fixed– Household interactions modeled

Starts with an assumed schedule skeleton– Represents routine, fixed activities– Considered the highest priority

Sequentially attempts to add new activities in order of assumed priority

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ALBATROSS modelScheduling process (continued)

Scheduling process– Ovals represents

decision steps

In ALBATROSS:– A skeleton schedule first

constructed containing routine activities

– Activity agenda is created

– Finally, activities added to the schedule from the activity agenda

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Source: Arentze, Timmermans (2000).

TASHA model overviewRoorda, Doherty, Miller (2005)

Activity scheduling model for Toronto area– Based on a 1996 household travel survey

Activities generated based on travel survey– Attributes assigned using choice models

Entirely rule-based– Activities fit to Project Agendas, then Schedule– Activities added to schedule based on assumed rules– Heuristics represent schedule adjustment, conflict resolution– Decision rules not well represented in the model

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TASHA modelScheduling process

Generate activity episodes– Activities generated based on trip diary data– Frequency, start time and duration chosen from

feasible portion of probability distributions

Fill each project agenda– Activities added to respective project agendas to form

provisional schedules within each project

Add activities to persons schedule– Activities added to schedule in order of priority– Priority assumptions: Work/School > Other >

Shopping and Joint activities > Individual activities– Adjust activities until conflicts removed

Generate activity

Add to project

agendas

Add to schedule

Fits inagenda

Yes

No

Fits inschedule

Next activity

Yes

No

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SIMTRAVEL

Integration of OpenAMOS ABM with MALTA DTA

Prism-constrained activity-travel scheduling (PCATS) – Kitamura et al. (1997)

Fill in prism gaps sequentially until no time-remaining between fixed activities

Source: SimTRAVEL website - http://urbanmodel.asu.edu/intmod/presentations.html

Research Gaps in ABM

Simplification of scheduling process– Priority rules, fixed order of choices

Short-term, diary data used– Most rule-based models implemented using 1-2 day diary data

Limited integration with traffic simulation– Static assignment for fixed time-periods– Ad hoc interoperability between model systems

Activity Generation/Planning/Scheduling Dynamics???

Scheduling Order Example

A) Impulsive Shop - Preplan Eat Out

Before Change

After Change

B) Preplan Shop - Impulsive Eat out

Before Change

After Change

ADAPTS - Introduction

ADAPTS activity-based model:– Simulation of how activities are planned and scheduled– Extends concept of “planning horizon” to activity attributes– Time-of-day, location, mode, party composition

Fits within overall framework of integrated land-use transportation model– Constraints from long-term simulation (land-use model)– Combined with route choice and traffic simulation

Core concept:– Universal set of activity planning / scheduling processes represented by heuristics

and/or models– Outcomes constrained by local context

ADAPTS Simulation Framework

Household Planning

Individual Planning

Household Schedule

Household Memory

Social Network

Individual Schedules

Individual Memory

Land Use

Network LOS

InstitutionalConstraints

Initialize Simulation• Initialize World• Synthesize Population• Generate routines

For each timestep

Write Trip Vector

Traffic Assignment

Information Flow

Simulation Flow

ADAPTS Planner/Scheduler

Handles at each timestep:– Generation– Planning– Scheduling

Each step can occur at different times for same activity

Core of the framework is the Attribute Plan Order Model

At timestep t

Generate new activity

Update existing activity(s)

Execute activity

Attribute Planning Order model

Planned Activity Schedule

Time-of-Day

t = Ttime

Party

t = Twith

Mode Choice

t = Tmod

Destination choice

t = Tloc

Executed Schedule

Resolve Conflicts

Conflict Resolution Model

Set Plan Flags:(Ttime,Tloc, etc.)

Yes

DecisionLogical testModel

Simulated events

Yes

No

Yes

No

No

Ac

tivity

G

en

era

tion

Ac

tivity

P

lan

nin

gA

ctiv

ity

Sc

he

du

ling

Decision Example:

T1Plan new activity- Ttime

- Tloc

- Twho-with

- Tmode

Ttime Tloc Tmode/who

Ttime

Plan time-of-dayTloc

Plan location

Twho = Tmode

Plan mode and who-with

Texecute

Texec

Execute Activity

Simulation Time

Schedule

At HomeTime: 12:00 AM – 8:00 AMLoc: HomeMode: None

WorkTime: 8:00 AM – 4:00 PMLoc: ?Mode: ?

ShopTime: ?Loc: ?Mode: ?

ShopTime: 4:00 – 5:00Loc: MallMode: Auto

??

WorkTime: 8:00 AM – 4:00 PMLoc: HOMEMode: None

Time:

Plan Work Location

Activity Generation

Activity PlanOrder Model

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References Arentze, T. and H. Timmemans (2000). ALBATROSS – A Learning Based Transportation

Oriented Simulation System. European Institute of Retailing and Services Studies (EIRASS), Technical University of Eindhoven.

Auld, J. A., and A. Mohammadian (2009). Framework for the development of the Agent-based Dynamic Activity Planning and Travel Scheduling (ADAPTS) model. Transportation Letters: The International Journal of Transportation Research, 1 (3), 243-253.

Bowman, J.L. and M.E. Ben-Akiva (2001). Activity-Based Disaggregate Travel Demand Model System with Activity Schedules. Transportation Research Part A. 35, 1-28.

Kitamura, R., C. Chen, C., and Pendyala, R.M. (1997) Generation of synthetic daily activity-travel patterns. Transportation Research Record, 1607, 154-162.

Pendyala, R.M.; R. Kitamura, A. Kikuchi, T. Yamamoto, S. Fujii (2005). Florida Activity Mobility Simulator: Overview and Preliminary Validation Results. Transportation Research Record: Journal of the Transportation Research Board, No. 1921, 123-130.

Roorda, M.J., S.T. Doherty and E.J. Miller (2005). Operationalising Household Activity Scheduling Models: Addressing Assumptions and the Use of New Sources of Behavioral Data. Integrated Land-use and Transportation Models: Behavioural Foundations, M. Lee-Gosselin and S.T. Doherty (eds), Oxford: Elsevier, pp. 61-85.

Yagi, S. and A. Mohammadian. (2008). Modeling Daily Activity-Travel Tour Patterns Incorporating Activity Scheduling Decision Rules, Transportation Research Record: Journal of the Transportation Research Board, No. 2076, TRB, National Research Council, Washington D.C., pp. 123-131.

Thank You!