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. 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. 3 C10A Model System  Model components exchange information in a systematic 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. 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. 5 Application Considerations  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 Planning Operations

6. 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. 7 Additional C10 Insights  Examples of sensitivity tests  Linkage vs integration  Equilibration and convergence  Consistency

8. 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. 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. 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. 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. 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. 13 Lessons Learned: Application  Level of convergence can significantly influence the conclusions drawn from alternative analyses.

14. 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. 15 Temporal Consistency  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 Base Spatial Detail

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

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

18. 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?