Act Now: An Incremental Implementation of an Activity-Based Model System in Puget Sound

1. Act Now:An Incremental Implementation of anActivity-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 • 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 • Long-Range • Dynamic traffic assignment • Continuous time • Weekend • Scenario evaluation tool

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 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

8. PSRC MODEL SYSTEM

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

10. KEY FEATURES • 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

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

12. ESTIMATION • 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

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 (<$30K, $30K-$60K, $60K-$100K,>$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 WITH4-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 WITH4-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. ACTIVITY GENERATOR:GPS ADJUSTMENTS • 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

26. ACTIVITY GENERATOR:GPS ADJUSTED TRIPS

27. ACTIVITY GENERATOR:TRIP GENERATION vs. ACTIVITY GENERATION

28. ACTIVITY GENERATOR:CALIBRATION & VALIDATION

29. 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