Enhancing Travel Demand Modeling: Insights from SHRP2 C10A Findings

1. SHRP2 C10A Final Conclusions & Insights TRB Planning Applications Conference May 5, 2013 Columbus, OH Stephen Lawe, Joe Castiglione & John Gliebe Resource Systems Group

2. C10A Project Objectives • Current models are limited • Not sufficiently sensitive to the dynamic interplay between travel behavior and network conditions • Unable to represent the effects of policies such as variable road pricing and travel demand management strategies • Advanced model systems can better represent demand changes and network performance • Peak spreading, mode choices, destination choices • Capacity and operational improvements such as signal coordination, freeway management and variable tolls, TDM

3. C10A Model System • Model components exchange information in asystematic and mutually dependent manner • C10A model components • Daysim “activity-based” model • TRANSIMS network simulation model • MOVES • C10A linked model system implemented in both Jacksonville, FL and Burlington, VT • “Linked” not “Integrated”

4. How are the model system components linked? • Daysim activity-based model provides travel demand to TRANSIMS network simulation model • Minute-by-minute • Parcel-to-parcel • Detailed market segments (toll/notoll, trip-specific VOT) • 1 hour to simulate 1 million people on laptop, ½ hour on server • TRANSIMS provides information on network performance by time-of-day, as detailed as: • 10 minute skims • Activity locations • ~50 VOT classes in assignment • “Studio” controls model system execution and equilibration

5. Application Considerations Planning & Operations • Different policy questions require different methods for running the model system • Disaggregate framework • Supports more detailed analysis • Extracting, managing and interpreting results is straightfoward • Volume of information is significant • Simulation variation • Not an issue for activity-model • Significant issue in network simulation Planning Operations

6. Conclusions • Integrated model system • is more sensitive to a wider range of policies • produces a wider range of statistics of interest to decision-makers • Level of effort required to effectively test different types of improvements varied widely • Debugging the model system, and individual scenarios was the greatest challenge • Must have willingness to investigate and experiment

7. Additional C10 Insights • Examples of sensitivity tests • Linkage vs integration • Equilibration and convergence • Consistency

8. Freeway Tolling: Demand Impacts • Trips shift out of peaks and midday and into evening and early AM • Higher tolls increases the magnitude of this shift • Time shifting varies by purpose • Work trips shift into early AM and out of AM peak • Social/recreation trips shift significantly out of peaks and primarily into the evening

9. Travel Demand Management • “Flexible Schedule” scenario • Asserted assumptions about: • Fewer individual work activities • Longer individual work durations • Aggregate work durations constant • Target: Fulltime Workers

10. Linkage vs Integration • Establishing linkages, not true integration • C10 goal of working with the existing tools and capabilities • Integration may require more fundamental reformulations • “Demand” vs “Supply Models • Demand models as “planning models” – most build schedule a priori, and don’t reflect time-dependency throughout the day • DTA as “dynamic models” • Mathematical formulations and behavioral theory • Lack of unifying behavioral theory • Differences in formulation and foundations between demand and supply models. • Mathematical formulations should follow behavioral theory

11. Linkage Challenges • Equilibration & Uniqueness • Unclear how to address within the context of complex simulation tools • Relevance to linked, advanced demand and supply models • Relevance to reality? • Need to consider multiple metrics • Gap • Consistency • Stability • Practical issues of network supply runtime

12. Convergence Testing • Convergence • Necessary to ensure usefulness of model system • Given the same inputs, will the model system produce the same outputs? • Can significantly influence the conclusions drawn • Network and system convergence • Extensive testing of different strategies • Network temporal resolution • Successive iteration feedback • Subselection

13. Lessons Learned: Application • Level of convergence can significantly influence the conclusions drawn from alternative analyses.

14. Consistency • Convergence not meaningful if there are egregious inconsistencies • Temporal • Spatial • Typological • Example: demand model employs trip-segmented VOT, but then a single VOT used in network model • Activity models (typically) • (Relatively) coarse temporal resolution • Typological detail • Dynamic network models (typically) • Temporal detail • Coarse typological resolution

15. Temporal Consistency Base • Even if consistent in structure or resolution, there can still be issues with outcome consistency • Ensure that the detailed schedules produced by the DaySim model are maintained in the TRANSIMS network model • Inconsistencies are inevitable – how to resolve • Maintain activity durations or departure times? • Allow supply model to reschedule Spatial Detail

16. Transferability Estimated difference between Tampa and Jacksonville coefficient estimates % of coefficients by type of choice model

17. Transferability Estimated difference between Tampa and Jacksonville coefficient estimates % of coefficients by type of variable

18. Future Efforts • Reconsideration of the fundamental “demand-supply” linkage • How can models be more tightly integrated? • Can integrated solution methods be defined? • Does equilibrium exist in reality, and if not what are the implications? • How can advanced models be implemented and applied most effectively?