Act Now:An Incremental Implementation of an

Activity-Based Model System in Puget Sound

Presented to:12th TRB National Transportation Planning Applications

ConferenceMay 19, 2009

Presented by:Maren Outwater, PSRC

Chris Johnson, PSRCMark BradleyJohn Bowman

Joe Castiglione

PRESENTATION OVERVIEW

PSRC model development strategy Activity-based models Activity generator technical approach Model calibration & validation Model application

PROJECT CONTEXT:PSRC MODEL DEVELOPMENT

Short-Range•Expand time periods•Expand purposes•Expand modes•Calibrate

Long-Range•Dynamic traffic assignment•Continuous time•Weekend•Scenario evaluation tool

Mid-Range•Develop activity-based travel demand model•Replace land use models•Integrate economic, land use, activity-based models•Benefit-Cost Analysis Tool•EPA MOVES/Mobile models

4-STEP MODEL LIMITATIONS

Insensitive to Interactions among trips, tours (trip chains) Interactions among persons in HH

Aggregation biases Demographic / market segmentation Temporal Spatial

Unable to answer key policy questions Insensitive in trip generation to pricing and

climate change policies

ACTIVITY-BASED MODELS ADVANTAGES

Better policy sensitivities Broader More behaviorally accurate

Consistency Within person-day of travel Across persons in a household

More detailed information Travel choices Impacts on travelers

ACTIVITY-BASED MODEL PROJECTS IN THE U.S.

94 95 96 97 98 99 00 01 02 03 04 05 06 07 08 09 10 11

San Francisco County (SFCTA)

Use

Tune

Build

KEY

Portland (Metro)

New York (NYMTC)

Columbus (MORPC)

Sacramento (SACOG)

Lake Tahoe

Atlanta (ARC)

Denver (DRCOG)

San Diego (SANDAG)

Seattle (PSRC)

San Francisco Bay Area (MTC)

AN INCREMENTALAPPROACH

Replace parts of trip generation with activity-generator

Integrate with current and new models Build upon PSRC model design,

enhancement and development efforts Implement quickly

PSRC MODEL SYSTEM

INTEGRATE W/ CURRENT MODEL

Land Use Allocation (Urbansim) Synthetic population Usual workplace

location

Zonal Data Distribution

Policy Sensitivity Transportation Land use

Induced/suppressed demand (accessibility via logsums)

Broader set of HH and individual attributes incorporated

Transition to full activity-based model

KEY FEATURES

ACTIVITY PURPOSES

Work Usual & other

School By age group

Escort (pick up / drop off) Shopping Personal business Meal Social / recreational

2006 HH Survey Processed into tours, trips, activity patterns Expanded, re-weighted

Discrete choice logit models Vehicle availability Out-of-home activity purposes Number of primary tours Number of work-based tours Number, sequence, purpose of intermediate stops

ESTIMATION

IMPLEMENTATION

Microsimulation models Household vehicle availability Person activity generation

Stochastic application for all HHs / persons in synthetic sample

Initially in Delphi, translated to Python Integration into overall model runstream

ACCESSIBILITY MEASURES:MODE & DESTINATION CHOICE LOGSUMS

Pre-calculated by Activity Generator Mode choice logsums

Based on existing trip-based mode choice models Segmented by purpose, income, auto availability Used in destination choice modes

Destination choice logsums Activity Generator uses destination choice models to pre-

calculate mode/destination accessibility logsums for residence zones.

Re-calculated at beginning of each global feedback iteration

SYNTHETIC POPULATION

Synthetic population input to vehicle availability and activity generator model

Produced by Urbansim (also predicts usual work locations)

Based on 2000 Census PUMS Distributions regionally controlled:

Household size (1,2,3,4+)

Household workers (0,1,2,3+)

Household income (<$30K, $30K-$60K, $60K-$100K,>$100K)

3.45 million regional residents

SYNTHETIC POPULATION:CALIBRATION & VALIDATION

0

200,000

400,000

600,000

800,000

1,000,000

1,200,000

1,400,000

1,600,000

Full-timeworker

Part-timeworker

Retired Non-worker Universitystudent

Student 16+ Student 5-15 Person under5

Person Type

Tota

l Pe

rso

ns

SurveySynthetic Pop

VEHICLE AVAILABILITY

Predict number of motorized vehicles used by household (own, lease, other) 0,1,2,3,4+

Key inputs HH attributes Home-work mode choice logsums Usual work location accessibility information Residence location accessibility information Vehicles vs. potential drivers

VEHICLE AVAILABILITY:CALIBRATION & VALIDATION

Observed data: 2006 PSRC Household Survey

DAY PATTERN MODEL Jointly predicts for each person:

Number of tours by purpose Occurrence of additional stops by purpose

Allow substitution between making additional tours and additional stops

Balance between person-day-level and tour-level sensitivities Example: Shopping Good access to stores -> spread shopping across

multiple stops and multiple tours Poor access to stores -> concentrate shopping

within fewer stops

DAY PATTERN MODEL Key inputs

HH attributes Person attributes Residence land use and accessibility Workplace land use and accessibility

Utility components Purpose-specific More tours and stops, regardless of purpose Purpose interaction effects

• Tours and tours• Tours and stops• Stops and stops

DAY PATTERN MODEL

Exact number of tours by purpose Number and purpose of work-based

subtours Number and purpose of

intermediate stops Usual workplace location vs other

work location

INTEGRATION WITH

4-STEP PROCESS

Activity generator replaces parts of trip generation step

Integrated into model system run stream as an executable

Activity generator outputs are converted to trip arrays for use in subsequent use in distribution, mode choice, assignment

INTEGRATION WITH

4-STEP PROCESS Activity-based model outputs converted to

trip-based model trip purposes HB Work HB School HB College HB Shop HB Other NHB Work : simple “origin choice” models predict

production end NHB Other: simple “origin choice” models predict

production end

ACTIVITY GENERATOR:CALIBRATION & VALIDATION

Goals Replication of key aspects of travel Reasonable regional network assignment

results GPS-adjusted targets

Under-reporting of trips in HH survey HH subsample vehicle-based GPS

Adjust for under-reporting of travel Limitations

Vehicle-based trips and HHs only Missing purpose information

Model developed to predict probability that given type of trip was missing Binary logit Based on HH and trip attributes Probability converted into adjustment

factor Factors constrained

ACTIVITY GENERATOR:GPS ADJUSTMENTS

ACTIVITY GENERATOR:GPS ADJUSTED TRIPS

ACTIVITY GENERATOR:TRIP GENERATION vs. ACTIVITY GENERATION

ACTIVITY GENERATOR:CALIBRATION & VALIDATION

ACTIVITY GENERATOR:CALIBRATION & VALIDATION

MODEL APPLICATION:TRANSPORTATION 2040

Regional Transportation Plan update Integrated model system

Puget Sound Economic Forecasting model Urbansim Activity Generator-enhanced 4-step model

TRANSPORTATION 2040:ALTERNATIVES

Alt 1: Existing system efficiency Alt 2: Capital improvements Alt 3: Core network expansion and

efficiency Alt 4: Transportation system management Alt 5: Accessibility and reduced carbon

emissions

TRANSPORTATION 2040:ALTERNATIVE INVESTMENTS

TRANSPORTATION 2040:EVALUATION CRITERIA

Mobility Finance Growth Management Economic Prosperity Environmental Stewardship Quality of Life Equity

TRANSPORTATION 2040:VEHICLE AVAILABILITY

TRANSPORTATION 2040:ACTIVITY GENERATION

TRANSPORTATION 2040:VEHICLE AVAILABILITY & ACTIVITY GENERATION

CONCLUSIONS

Activity generator can replace trip generation in a 4-step model

Data requirements comparable to traditional trip generation

Can be implemented and calibrated quickly and efficiently

Provides enhanced model sensitivities, though effects were modest