Transit Stop Level Ridership Model Deployment

1. Lessons Learned in Transit Stop Level Ridership Model Deployment 12th TRB National transportation Planning Applications Conference May 20, 2009 Steve Polzin & Mark Mistretta, Center for Urban Transportation ResearchRodney Brunner, Gannett Fleming, Inc.

2. Outline • Description and History of TBEST • User Needs • Lessons Learned • Future Development

3. What is TBEST • Transit Boardings Estimation and Simulation Tool (TBEST) is 3rd generation • 1st generation: Integrated Transit Demand and Supply Model (ITSUP) • 2nd generation: Regional Transit Feasibility Analysis and Simulation Tool (RTFAST)

4. TBEST has been Around a While

5. Nature of TBEST • Direct Demand Model • Not mode choice • Not interactive with auto travel • Models at stop level but most useful at route segment level • Designed specifically for transit • Walk access scale • Captures transit network accessibility

6. Operational Changes Short turning Route splitting Through routing Schedule Changes Running time adjustments Headway adjustments Schedule coordination between routes Span of service adjustments Alignment Changes Extending routes Shortening routes New routes System Changes Service redistribution Network structure Fare Changes First-boarding fare Transfer fare Responsive to Service Changes

7. Modeling and Prediction Framework • Stop-Level Analysis • Physical location • Route • Direction • Multiple Analysis Periods • 4 on weekdays • Saturday • Sunday • Relationships in a Transit Network • Neighboring stops • Accessible stops • Accessibility measures/transfer potential • Separate Direct and Transfer Boardings

8. Variables Used in Boarding Equations • Origin Buffer Characteristics (direct boarding) • Accessibility Measures • Impedance from origin to accessible stops • First fare and transfer fare • In-vehicle time • First and transfer waiting • Number of transfers • Transfer walk time • Population/employment in accessible stop buffers • Transfer Potential (transfer boarding) • Route and Stop Service Characteristics

9. Provided Data Inputs for Florida Agencies • 2000 Census Data with Pre-Formatted SF1 and SF3 Variables • 2000 InfoUSA Employment Data Grouped by Commercial, Industrial, and Service • 2000 GDT Street Networks • 2006 Pre-Coded Transit Networks

10. Basic Steps for Applications • Base Scenario • Develop Transit System Network • Enter other input data (interliners, transfer centers, …) • Update provided socio-demographic to base year • Run base scenario • Base Validation • Enter observed ridership data (system, route, etc.) • Run validation • Develop Alternative Scenarios • Run Alternatives • Compare • Ridership, Performance Measures

11. User Needs and Capabilities FDOT/planners wanted a software tool for short-term transit service planning Desired something designed with transit in mind – i.e. walk access sensitive Serve as FDOT provided ridership estimation technique for TDPs User friendly Non-GIS expert compatible Brings consistency and embeds knowledge in model data and coefficients

12. Lessons Learned – Understanding Users • Capabilities of agency planning staff • Knowledge of service planning • Knowledge of GIS • Knowledge of data bases • Resources available locally • Time to learn and use model • It is not clear that agency staff are sufficiently experienced to integrate model results and expert judgement

13. FDOT and Project Team Response • Data bases embedded in model for Florida • Training offered • Web site discussion group • Technical support built into model development/maintenance efforts • Some ongoing model enhancements programmed (updates for ARCGIS versions, fixes to identified problems, etc.)

14. Lessons Learned – Why Change? • Why plan future service when we don’t have money to operate it? • I heard the model didn't work in city xxxx therefore we don’t want to use it. • We don’t have dedicated staff, any GIS experience, money to travel to training etc. • I heard you were changing the model. (rumor has it Microsoft keeps changing their software)

15. Lessons Learned – Staff vs Consultants • Consultants offer strong methods and breadth of experience. • Consultant approach fails to fully integrate the modeling capability within staff which enables it’s use for other applications. • The expertise and context knowledge to do quality service planning is often lacking in small or fast growth areas.

16. User Expectations for Accuracy and Precision • The challenge of stop level data is the temptation to value the model based on the accuracy at the stop level. • Transit agencies like stop level accuracy yet often don’t have stop levels counts to compare forecasts to. • “Transit trip Attractions” are difficult to appropriately value with current socio-demographic and employment data bases.

17. Model Ownership and Governance • Who should own/support the software and documentation? • Florida has been very supportive • Other entities have helped • Lack of permanent funding commitment or mechanism for shared funding • Highly dependent on a single consultant/person • Flexibility, exclusivity • Transparency – intellectual property

18. Amortizing Model “Costs” over Multiple Uses Justifying investment by using tool for more purposes • Accessibility / equity analysis • Data inventory tool • Operating cost model/tool • Land use development impact assessment tool

19. Future Development Priorities Client Driven: • Future pop/employment scenarios tools • Corridor focus feature – boundary treatments, run time improvements • Stronger treatment of special generators • Calibration for additional modes • Lrt • Brt • Metro • People mover • Commuter rail

20. Future Development Priorities Researcher Driven: • Variable Special Generator Variable (units of trip ends?) • Input data precision improvements

21. Future Development Priorities Researcher Driven: • Park and Ride Model Overlay • Walk access  Roadway access • Enhance trip production/attraction data for non-home end • Data and knowledge of trips at home end well understood with modest range of variation • Knowledge of work or non-home end highly variable and with far less data available

22. “The model said I took the bus here.” “You’ve either got dirty data or you are in the error term.”

23. TBEST Version 3.2 - coming soon • Latest developments – • Online Map Access • Expanded Socio-economic Growth Capabilities • Sector/Sub-area scenarios • Expanded Model Capacity • Loaded Network Output • Improved Map Draw Speed • Many other improvements

24. TBEST Website • User Manuals/Guides • Download Software • Publications/Reports • User Forum / Ask Questions • Request Support • TBEST Website - http://www.tbest.org/