1. Act Now: An Incremental Implementation of an Activity-Based Model System in Puget Sound Presented to: 12th TRB National Transportation Planning Applications Conference May 19, 2009 Presented by: Maren Outwater, PSRC Chris Johnson, PSRC Mark Bradley John Bowman Joe Castiglione

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

3. 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. 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

5. 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

6. ACTIVITY-BASED MODEL PROJECTS IN THE U.S.

7. AN INCREMENTAL APPROACH 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

8. PSRC MODEL SYSTEM

9. INTEGRATE W/ CURRENT MODEL Land Use Allocation (Urbansim) Synthetic population Usual workplace location Zonal Data Distribution

10. 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

11. ACTIVITY PURPOSES Work Usual & other School By age group Escort (pick up / drop off) Shopping Personal business Meal Social / recreational

12. 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

13. 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

14. 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

15. 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 ( $100K) 3.45 million regional residents

16. SYNTHETIC POPULATION: CALIBRATION & VALIDATION

17. 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

18. VEHICLE AVAILABILITY: CALIBRATION & VALIDATION Observed data: 2006 PSRC Household Survey

19. 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

20. 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

21. 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

22. 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

23. 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

24. 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

25. 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

26. ACTIVITY GENERATOR: GPS ADJUSTED TRIPS

27. ACTIVITY GENERATOR: TRIP GENERATION vs. ACTIVITY GENERATION

28. ACTIVITY GENERATOR: CALIBRATION & VALIDATION

30. MODEL APPLICATION: TRANSPORTATION 2040 Regional Transportation Plan update Integrated model system Puget Sound Economic Forecasting model Urbansim Activity Generator-enhanced 4-step model

31. 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

32. TRANSPORTATION 2040: ALTERNATIVE INVESTMENTS

33. TRANSPORTATION 2040: EVALUATION CRITERIA Mobility Finance Growth Management Economic Prosperity Environmental Stewardship Quality of Life Equity

34. TRANSPORTATION 2040: VEHICLE AVAILABILITY

35. TRANSPORTATION 2040: ACTIVITY GENERATION

36. TRANSPORTATION 2040: VEHICLE AVAILABILITY & ACTIVITY GENERATION

37. 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