The transition to activity-based models in the U.S.

Mark Bradley Bradley Research & Consulting

Santa Barbara, CA

Approaches to activity-based travel demand modeling

Priority on temporal activity schedules- ALBATROSS, CHASE, FAMOS, …

Priority on spatial agents and networks- TRANSIMS, Nagel et al., …

Priority on econometric choice structures- Bowman and Ben-Akiva Vovsha, et al. Bhat, et al.

Key Concepts

Tour-based and activity-based Microsimulation of individuals,

which enables… Disaggregation at many levels,

which provides… More useful and behaviorally

realistic models for policy analysis

Model structure (tours and full day patterns)

Method of implementation (microsimulation)

How activity-based models are different from trip-based

Traditional trip-based structure

Auto ownership (some) Trip generation Trip distribution / destination

choice Trip mode choice (most) Trip time of day (some) Network assignment

Concept of ToursHomeHome

Coffee StopCoffee Stop

WorkWork

LunchLunch

Stop at StoreStop at Store

Tour-based: Add tour-level models Auto ownership . Tour generation Tour main destination choice Tour times of day Tour main mode choice . Trip generation (intermediate stops only) Trip destination (intermediate stops only) Trip mode choice (usually same as tour mode) Trip time of day (may use shorter periods) Network assignment

Activity-based: add person-day level

Usual work and school location Auto ownership . Day-pattern: consistent generation of tours

(subtours) for all activity purposes Tour main destination choice Tour times of day (consistent scheduling) Tour main mode choice . Trip generation (intermediate stops only) Trip destination (intermediate stops only) Trip mode choice (usually same as tour mode) Trip time of day (may use shorter periods) Network assignment

Person-day level decisions

Key model design issue – number of activity/tour purposes Mandatory out-of-home

1. Work2. School (K-12 or university, depending on age)Non-mandatory out-of-home3. Escort (pick up/drop off passenger)4. Personal business (including medical)5. Shopping6. Meals7. Social / recreation

Individual Day Activity Pattern (DAP) Model

Model can include all relevant combinations of: Number of tours by purpose (all models) Presence of extra stops by purpose (some

models) Allocation of stops to particular tours (some

models) Presence of work-based subtours (most

models) Key in-home activities (very few models)

Use of consistent time windows Simulate tours in priority order “Block out” time periods as they are used Use endogenous “time pressure” variables

to influence activity scheduling With short enough time periods, can

enforce time/space constraints

Some models also include intra-household interactions Coordination of day pattern types

across household members Treatment of fully joint

tours/activities made by multiple household members

People driving other household members to work or school

Levels in activity-based models

Longer term household / person level decisions

Household-day level decisions

Person-day level decisions

Tour level decisions

Trip / stop level decisions

0

1960's 1970's 1980's 1990's 2000's

State of the practice

State of the theory

Standard vs. Ideal

Land use projections

Trip-Based (“4 step”) Trip generation Time of day factors Trip distribution Trip mode choice

Traffic assignment

Land use microsimulation

Activity- and Tour-Based Full day activity

participation Full day activity

scheduling Activity location choice Tour and trip mode choice

Traffic microsimulation

Microsimulation of individuals

Simulate each “individual” in the population separately (can use expansion/replication factors)

Use stochastic “Monte Carlo” procedure to sample discrete choices from choice probabilities

Aggregate vs. Microsimulation

“Top down” Production zonesX Population

segments X Trip purposesX Destination zonesX ModesX Time periods= Can be billions of

combinations

Aggregate into most convenient categories for

Traffic assignmentEquity analysis, etc.____________________

Millions of individual-levelsimulated full day activity

and travel patterns_____________________“Bottom up”

A “simulated travel and activity diary” for the entire regional population.

Detailed in time and space for input to traffic micro-simulation

Can be aggregated to trip matrices for zone-based network assignment

Can be aggregated along other dimensions for other types of analysis, such as equity analysis

Activity-based model output

U.S. Activity-Based Models in Use

ColumbusColumbus

San FranciscoSan Francisco

SacramentoSacramento

New YorkNew York

U.S. Activity-Based Models in Use and Under Development

ColumbusColumbus

San FranciscoSan Francisco

DenverDenver

SacramentoSacramento

AtlantaAtlantaDallasDallas

New YorkNew York

Bay AreaBay Area

OregonOregon

U.S. Activity-Based Models in Use, Under Development, and Proposed

OregonOregon

ColumbusColumbus

San FranciscoSan Francisco

ChicagoChicago

DenverDenver

SacramentoSacramento

AtlantaAtlantaDallasDallas

MichiganMichigan

HoustonHouston

PhoenixPhoenix

Los Los AngelesAngeles

SeattleSeattle

TampaTampa

New YorkNew York

Bay AreaBay Area

The majority of new models developed for major MPO’s are now activity-based

0

1960's 1970's 1980's 1990's 2000's

State of the practice

State of the theory

State of advancedpractice

Claimed advantages of activity-based modeling (1)

They can take advantage of recent advances in GIS and computing capabilities

They are sensitive to a wider range of policies (various types of pricing, peak spreading, telecommuting/TDM, parking) and demographic shifts.

They are able to represent detailed land use patterns and the effects on non-motorised travel

They are able to accommodate a much finer level of disaggregation temporally, spatially, demographically (e.g. distributed VOT), and in terms of typology of activities.

Sacramento- Aggregate vs. Microsimulation

SACMET SACSIMHH size, income >> All Census person and

segmentation household characteristics 6 trip purposes >> 7 activity purposes8 travel modes >> 8 travel modes 1,300 zones >> 700,000 parcels4 time periods >> 48 half-hour time periods

Much more detail without much increase in run time (except for assignment)

Using a Two Level Spatial System

Zone level Used for O-D-level of service matrix data Output for standard traffic assignment

Parcel level Used for transit access walk times & short

walk, bike, auto times Used for pedestrian, urban design variables Used for more detailed land use and density

measures

Model variables that take advantage of the parcel level

Walk time from parcel to transit stop Parcel-to-parcel distance for short trips Street network density within ½ mile buffer

Retail job density within ½ mile buffer

Mixed use density within ½ mile buffer

Parking supply and price within ½ mile buffer

Non-auto mode shareby Density w/in ¼ Mi. of HH

0%

5%

10%

15%

20%

25%

30%

35%

40%

45%

50%

<=4.0 4+ to 10 10+ to20

20+ to40

>40

Total Density {(jobs+du's)/acre}

% o

f T

rip

s b

y W

alk

,Bik

e,T

ran

sit

SACSIM

c

Non-auto mode shareby Density w/in ¼ Mi. of HH

0%

5%

10%

15%

20%

25%

30%

35%

40%

45%

50%

<=4.0 4+ to 10 10+ to20

20+ to40

>40

Total Density {(jobs+du's)/acre}

% o

f T

rip

s b

y W

alk

,Bik

e,T

ransit

Survey SACSIM

c

VMT / HHby Density w/in ¼ Mi. of HH

0

5

10

15

20

25

30

35

40

45

50

55

60

<=4.0 4+ to 10 10+ to 20 20+ to 40 >40

Total Density {(jobs+du's)/acre}

Vehic

le M

iles T

ravele

d P

er H

ousehold

SACSIM

VMT / HHby Density w/in ¼ Mi. of HH

0

5

10

15

20

25

30

35

40

45

50

55

60

<=4.0 4+ to 10 10+ to 20 20+ to 40 >40

Total Density {(jobs+du's)/acre}

Vehic

le M

iles T

ravele

d P

er H

ousehold

Survey SACSIM

Claimed advantages of activity-based modeling (2)

They are able to represent time-of-day shifting and activity scheduling effects.

They provide results that can be used in a wider variety of contexts, including environmental justice analysis, traffic microsimulation models, and land use microsimulation models

Applications of San Francisco County model (CHAMP) County long range transportation

plan “New Starts” analysis Corridor level analysis, with detailed

transit assignment, traffic simulation Environmental Justice (EJ) analysis Model recalibration to new 2000 data Downtown cordon/area time-of-day

pricing analysis (in progress)

Applications of New York BPM Regional air quality conformity analysis Several “New Starts” transit investment

studies Several feasibility and pricing studies for

major bridges and tunnels Manhattan area pricing study (in progress),

including extensive social equity analysis Major multi-modal corridor study (West

Hudson) Results fed into traffic planning studies for

over 30 local agencies and projects

Columbus (MORPC) model applications Regional air quality conformity

analysis A “New Starts” LRT/BRT investment

study Several corridor studies for highway

extensions Central business district parking

study

Sacramento (SACOG) model applications Regional air quality conformity

analysis A “New Starts” LRT investment study Parking and transit plan for

Sacramento State University A “4 D’s” study (density, destination,

design, diversity) Integration with PECAS land use

microsimulation model

Claimed advantages of activity-based modeling (3)

They are less of a black box and more intuitive to users and policy makers.

Demonstration tools for policy studies Support a wider range of descriptive analyses

(similar to analysis of travel survey data) They provide more realistic and accurate

aggregate forecasting sensitivities/elasticities.

Where do we go from here? Keep making models faster and easier to

use Better utilities for data preparation and

output querying Assemble and assess evidence on

forecasting results over several years (Ohio DOT before-and-after validation project)

Prioritize most beneficial model features in the context of planning needs

Where do we go from here? (2)

Incorporate findings from academic research (more general econometric models, time budget constraints, demand/supply equilibration

Explicit dynamics of shifts in individual activity/travel patterns

Better integration with land use simulation and traffic simulation models

Types of data sources Road networks and capacities Transit networks, fares and schedules Census and PUMS/ACS data Economic/employment data School enrollment data GIS database (parcel/point level best) Traffic screenline counts and speed data Transit passenger counts

Household travel/activity diary survey

Replicability of Results In aggregate models and probabilistic models

applied using probabilities directly, results are same every time model is run

When Monte Carlo simulation is used, results differ (unless random number seed is kept constant)

To obtain “average” results, need to run model several times:

Castiglione et al suggest that 10-20 runs are needed to stabilize at the zone level, 5-10 runs for neighborhoods

Number of runs will vary depending on level of detail

Time and budget… Typical project requirements:

1 - 2 years (after data is available) $300K - $900K for calibrated model

system

Hardware and run time issues are becoming less important as computers and software improve

Accessibility linkages to upper level models (upward integrity)

Work and school > can use person-specific mode choice logsums for the usual location

Other travel purposes > can use pre-calculated zonal level mode/destination choice logsums by segment: Transit accessibility band (subzone) Auto availability/competition HH income

Controls for Synthetic Sampling

3 variables used most places (in CTPP 1-75) Household size (1, 2, 3, 4+) Workers in HH (0, 1, 2, 3+) HH income (0-25, 25-50, 50-75, 75+)

Other possible variables Age of head of HH Presence (0/1+) of children under age 18 Presence (0/1+) of adults over age 65 Family / non-family HH

Group quarters treated as a separate segment?