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 Tours Home Coffee Stop Work Lunch Stop 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. Work 2. School (K-12 or university, depending on age) Non-mandatory out-of-home 3. Escort (pick up/drop off passenger) 4. Personal business (including medical) 5. Shopping 6. Meals 7. 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

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 zones X Population segments X Trip purposes X Destination zones X Modes X Time periods = Can be billions of combinations Aggregate into most convenient categories for Traffic assignment Equity analysis, etc. ____________________ Millions of individual-level simulated 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 Columbus San Francisco Sacramento New York

U.S. Activity-Based Models in Use and Under Development Columbus San Francisco Denver Sacramento AtlantaDallas New York Bay Area Oregon

U.S. Activity-Based Models in Use, Under Development, and Proposed Oregon Columbus San Francisco Chicago Denver Sacramento AtlantaDallas Michigan Houston Phoenix Los Angeles Seattle Tampa New York Bay Area The majority of new models developed for major MPO ’ s are now activity-based

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 SACMETSACSIM HH size, income >>All Census person and segmentationhousehold characteristics 6 trip purposes>>7 activity purposes 8 travel modes>>8 travel modes 1,300 zones >>700,000 parcels 4 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 share by Density w/in ¼ Mi. of HH

VMT / HH by Density w/in ¼ Mi. of HH

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 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: 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?