1. SAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY San Francisco DTA Project: Model Integration Options Greg Erhardt DTA Peer Review Panel Meeting July 25 th, 2012

2. SAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY Agenda 9:00Background 9:30Technical Overview – Part 1 Development Process and Code Base/Network Development 10:15Break 10:30Technical Overview – Part 2 Calibration and Integration Strategies 12:00Working Lunch / Discussion 2:00Panel Caucus (closed) 3:30 Panel report 5:30Adjourn

3. Outline Foundations Extracting LOS from DTA Feeding LOS to SF-CHAMP Transit Integration Feeding Additional Information to DTA Key Questions and Next Steps 3

4. Foundations

5. Goals of Integration Better consistency between supply & demand models to avoid unreasonable gridlock Better LOS information for demand models Temporal differences Operational details Reliability Better demand information to DTA User heterogeneity 5

6. Temporal Resolution 6 Integration of ABM and DTA (Direct) Integration of ABM and DTA (Aggregate Feedback) Source: SHRP2 L04 Draft Report December 2011

7. Convergence & Stability Averaging Methods MSA or similar Applicable where continuous variables are being averaged Enforcement Methods Unique to microsimulation Examples: Gradually freeze a portion of households or decisions Analytically discretizing mode choices 7

8. Extracting LOS from DTA

9. Evaluate Levels of Temporal Resolution At what level do travelers think about time and cost? 1 hour? 15 minutes? 1 minute? How much do travel times and cost change within 15 minutes? 1 hour? 3 hours? 9

10. Spatial & Temporal Expansion SF-CHAMP covers 9-county area, so need to merge DTA skims with static Currently only modeling PM peak—use 24-hour DTA for feedback of all periods 10

11. Measure Reliability Could attempt to measure reliability within time periods Could attempt to measure reliability across days Reference SHRP2- L04 11

12. Feeding LOS to SF-CHAMP

13. Integrate with Trip Time-of-Day Model Existing trip TOD model based on time shift from preferred departure time Uses static ½ hour skims Autos only Could substitute dynamic skims 13 Effect of Time Shift on Utility

14. Feedback Skims in Existing 5 Time Intervals SF-CHAMP currently accepts skims based on 5 periods: Early AM, AM Peak, Midday, PM Peak, Night Tour TOD model operates at this resolution DTA times could be averaged to these periods Alternately, logsums from a trip TOD model could be used and fed back to SF-CHAMP at this resolution 14

15. Feedback Skims with More Disaggregate Time Intervals Feedback dynamic skims in 1 hour, 30 minute, or 15 minute resolution Would require replacing SF-CHAMP’s tour time-of-day model Provides additional sensitivity to time-of-day differences in upstream models 15

16. Feedback Individual Vehicle Trajectories 16 Source: SHRP2 L04 Draft Report December 2011 Possible Scheme for Fully Disaggregate Integration

17. Fully Disaggregate ABM-DTA Integration Fully disaggregate models on both ends could allow: Fully consistent daily schedule for each traveler, adapting to differences in planned versus actual travel times Moving unit of analysis in DTA from trip to a tour, allowing for the timing of stops to be accounted for in the DTA Possible representation of user heterogeneity in DTA Sampling of alternatives removes dependency on TAZs and allows any level of spatial disaggregation 17

18. Behavioral Considerations Aggregate Feedback Assumes day is planned ahead based on average conditions All possible zones/time periods included in feedback Upstream model decisions based on averages or logsums Disaggregate Feedback Allows adaptation based on conditions encountered during the day Sampling of alternatives assuming limited traveler information Upstream model decisions based on specific travel times 18 How do we think people make decisions?

19. Practical and Policy Considerations How to integrate with transit, and transit time-of-day choice? Would the result be stable across scenarios? Would additional disaggregation propagate simulation noise? How to achieve convergence—averaging versus enforcement? Disaggregation could allow flexibility in spatial definitions Level of temporal aggregation should be related to policies being considered 19 What do we think is useful?

20. Transit Integration

21. Use Existing Transit Pathbuilder Several options: 1.Code transit trajectories in RUNTIME field 2.Attach average DTA link travel time to static network used for building transit skims 3.Parse out transit trajectories to individual links 4.Modify SF-CHAMP to accept transit skims for shorter time periods 5.Incorporate a transit trip departure time model 21

22. Implement Dynamic Transit Assignment Could use FastTrips Could incorporate: Dwell times based on boardings and alightings Bus bunching Delays due to roadway congestion 22

23. Feeding Additional Information to DTA

24. User Heterogeneity in DTA In SF-CHAMP, each traveler has their own value of time This could be incorporated into DTA through additional user classes Requires additional runtime Alternately, individual vehicles in the simulation could be assigned different values of time Requires restructure of DTA software 24

25. Key Questions & Next Steps

26. Key Questions Should we expand temporally (to 24-hours)? Should we expand spatially (to 9-counties)? At what temporal resolution do people make: Routing decisions? Path type choice (toll vs. no-toll) decisions? Trip departure time decisions? Mode choice decisions? Destination choice decisions? Tour and activity scheduling decisions? Tour and activity participation decisions? How can transit be modeled? How can reliability be measured? 26

27. Next Steps Evaluate what is feasible within this project Consider which approaches offer long-term promise 27

28. Questions?