MTC Travel Model Specification & Training Study Overview Chuck Purvis, MTC Bay Area Conformity Working Group October

1. MTC Travel Model Specification & Training StudyOverviewChuck Purvis, MTCBay Area Conformity Working GroupOctober 25, 2005

2. Consultant Study • $250,000 Budget [Phase I] • July 2005 – August 2006 • PB Consult • Joe Castiglione, Joel Freedman, Peter Vovsha, David Ory, Bill Davidson • Sub-Consultants • Frank Koppelman, Susan Stewart, Mark Bradley, Keith Lawton, Ken Train

3. Past MTC Forecasting • Tradition of developing and maintaining travel demand models “in-house” • Aggregate model from 1960’s • MTCFCAST – groundbreaking 1970’s models • BAYCAST-90 – currently in use • Extensive model application • RTP • Air quality conformity • MIS / Corridor studies • Policy alternatives analysis • Established user community

4. Future MTC Forecasting • Next generation of Bay Area travel models • Greater policy sensitivity and analysis capabilities • Implement methodological advancements • Transition from trip-based • Activity-based • Tour-based • Increase zonal and network detail • Integrate latest data

5. Study Purpose • Assist MTC staff in developing next generation of Bay Area travel models • Tasks: • Develop travel model specification plan • Provide model estimation training and support • Provide data processing support • Provide model implementation support

6. Increasingly Complex Policy Questions • “System efficiency” and “strategic enhancement” • Pricing strategies • Congestion pricing • HOT lanes • Technologies • Transit, New Starts • Telecommuting • Policy analysis needs • Demographic impacts • Transit Oriented Development • Trip models not sensitive to some key policy questions

7. Trip-based Models • “Traditional” travel forecasting approach • 4 steps (variations such as MTC’s 6 steps) • Generation – What kinds of trips? How many? • Distribution – From where to where? • Mode Choice – What mode? • Assignment – What specific route (transit, roadway)? • BAYCAST is an advanced trip-based model

8. Criticisms of Trip-based Models • Not internally consistent • Doesn’t address relationships / constraints among trips • Spatial • Temporal • Modal • Example: Non-home-based trips • Biased due to aggregation • Explanatory variables • Temporal • Spatial • Limited policy sensitivities • Time of day (pricing, peak spreading) • Non-motorized modes • Accessibility

9. Limitations of Trip-based Models • Example: HOT lanes • Not sensitive to: • Intra-HH tripmaking? • Most HOV trips made with members of same HH • Time-of-day? • Sufficient temporal detail? • Variable pricing? • Behavioral responses? Mode shifts?

10. Tour-Based Modeling • Most people travel to participate in activities • Tour-based models predict: • Activities • Locations • Times • Travel is implicitly forecasted

11. Pop Synthesis HH distribution Long-term choices Trip Generation Daily Activity Pattern • Tour: • Time-of-day • Destination • Mode Trip Distribution Mode Choice Time-of-day • Trip: • Destination • Mode • Departure Assignments Assignment Comparable Components Activity-/Tour-based Trip-based

12. Tour-based Modeling • Tour = entire sequence of trips from origin (anchor location) to all destinations, back to origin. • Primary destination, intermediate stops • No more non-home-based trips!! • Tour-based structure commonly used in activity-based modeling • Tour Purpose Classification - Hierarchical • Mandatory – Work, School • Maintenance – Shop (household), pickup/drop-off • Discretionary – Social/Recreational, Other

13. Tour-based Modeling Work-Based Tour Work Tour Eat lunch Go shopping at supermarket

14. Schematic Tour-basedModel Structure Pop Synthesis Daily Activity Pattern Tour Generation Time-of-Day Feedback Primary Location And Mode Intermediate Stop Location Trip Mode Assignment

15. Microsimulation • What is microsimulation? • Synthetic sample drawn that represents actual population • Travel explicitly modeled for each person/household • Monte Carlo simulation instead of fractional probability aggregation • Variable results

16. Micro-Simulation – Auto Ownership Example Monte Carlo Micro-Simulation > 0.3897 ? Household 1 Random Number Draw = 0.3897 = 2 autos

17. Microsimulation • Why use microsimulation? • Computationally efficient • Increased ability to include explanatory variables • Substantial reduction in aggregation error • Allows wide range of policy analysis: Lots of data!!

18. Microsimulation Microsimulation Models Trip-Based Models HID PID AUT INC WRK GEN AGE EMP 1 1 1 3 1 0 24 1 1 2 1 3 0 1 23 0 1 3 1 3 0 1 3 0 2 1 2 4 2 0 32 1 2 2 2 4 2 1 34 1 3 1 3 2 2 0 49 1 3 2 3 2 2 1 47 1 3 3 3 2 2 1 15 0 3 4 3 2 2 0 12 1 Data stored in matrix format Each market segment = new set of trip tables Data storage in tabular format Each market = new column

19. Advantages of Tour-based Modeling • Greater policy sensitivity • Transportation system performance results from combination of individual decisions • Policies are oriented at influencing the individual • Individuals and “individual” households and make decisions • Incorporate spatial and temporal constraints • Address more complex policy questions

20. Project Tasks • Assist MTC staff in developing next generation of Bay Area travel models • Tasks: • Develop travel model specification plan • Provide travel estimation training and support • Provide data processing support • Provide model implementation support

21. Develop Travel Model Specification Plan • Prepare comprehensive technical summary • Detail overall design and specification • Identify options • Structure / components • Software / hardware • Link model design to model estimation support

22. Model Specification Issues [I] • Population synthesis • Individual daily activity pattern • Intra-household interactions • Tour (round trip) as unit and tour purposes • Nonmotorized travel • Incorporate new attributes • Utilize existing data (GIS-based analyses, new networks)

23. Model Specification Issues [II] • Temporal detail (time-space constraints) • Spatial detail (smaller zones / grid-cells / link faces) • TAZs • Increased spatial resolution • More detailed pop/emp forecasts • Networks • Increased spatial & temporal resolution • Utilize existing data (GDT, RTD, Baycast) • Microsimulation • Runtimes • Feedback, stochastic results

24. Trip Chaining / Data Prep • Preparation of BATS2000 to support activity-based / tour-based models • Implement trip chaining procedures • BART sample integration

25. Model Estimation Training • Coordinate course development with MTC • Management staff collaboration • Modeling staff capability assessment • Design to meet staff/programmatic needs • On-site instruction • Practical exercises

26. Model Estimation Oversight • Support MTC staff during estimation • Review and discuss estimation results • Propose alternative models, structures and/or specifications • Support model troubleshooting

27. Model Implementation • Integrate project tasks • Operationalize: • Designs specified, using • Data developed, by • Applying instructed techniques

28. When is the New Model Ready? • Phase I – complete by August 2006 • Phase II – FY 2006/07 – may be needed to complete model estimation and model implementation • Base Year Validation (2000, 2005) to be completed by end of 2007 • 2008: Begin Use of Model System for Next Regional Transportation Plan

29. Questions?