1. Integrated Travel Demand Model Challenges and Successes Tim Padgett, P.E., Kimley-Horn Scott Thomson, P.E., KYTC Saleem Salameh, Ph.D., P.E., KYOVA IPC 2015 WV Planning Conference September 16 Davis, West Virginia

2. “There is really no hope that a mathematical model can ever accurately predict the future, given the uncertainty in demographics, technological shifts, and social changes.” – JD Hunt “But we still try.” – Tim

3. Forecasting Background Why do we use forecasting models?

4. Model Background History Model versions over time Previous model and transition to current

5. 2015 KYOVA Travel Demand Model

6. Useful Planning Tool CMP, MTP/TIP, corridor studies, and other applications Three Current Models KYOVA, Ashland, and RIC Limitations Not all are time-of-day Speeds and capacities calculated differently “Apples vs. oranges”

7. What’s Different Boundary Capacity methodology

9. Model Update Expand KYOVA model to include Ashland model Integrate HCM 2010 methods for speed, capacity Incorporate HERE travel time data Update external travel data Incorporate truck network, trip matrix V/C calculations Documentation and training

10. Highway Capacity Manual 2010 Free-flow speeds and capacities  traffic forecasts, V/C Major update underway Changes in methods (e.g., Urban Streets FFS prediction)

11. Additional Features Free-flow speed override Simplified user interface

12. Automatic Reporting RMSE VMT by county and functional class Congested speeds by county and functional class VHT by county and functional class

13. Travel Demand Model Integration Issues Network attributes -Integrate state linear referencing systems (Road analyzer, HIS, and LRS) Household model -Household composition and trip rates for additional counties Employment data -Coordinate employment classes and data sources Traffic counts -2010 daily, hourly, and truck counts for calibration and validation Trip distribution -Re-estimate gravity model parameters to reflect expanded area

14. Challenges and Successes Challenges Data Multiple jurisdictions Successes Coordination and cooperation

15. Thinking Beyond the Model

16. When the Model isn’t Enough Travel demand models don’t do a great job with: -Transit and Transit Suitability -Freight Planning -Alternatives Analysis Balance between complexity of model, ease of use, and cost

17. Mode Share What is it? District of Columbia Multimodal Long Range Transportation Plan What was the question? How much can we increase non-auto mode share by expanding transportation choices and improving the reliability of all transportation modes?

18. Mode Share How did we answer the question? Used elasticities to account for introduction of new service and expansion of existing service Used GIS to determine influence areas and applied this to our model trip tables to shift trips between modes

19. Mode Share

20. Results and Conclusions Answers would not have come from the travel demand model alone Interesting—showed that investment alone wasn’t enough to achieve their goal of 75% non-auto mode share

21. KYOVA’s Spatial Decision Support System (SDSS) Integration

22. What is a Spatial Decision Support System (SDSS)? “…an interactive, flexible, and adaptable computer- based information system, especially developed for supporting the solution of a non-structured management problem for improved decision making. It utilizes data, provides an easy-to-use interface, and allows for the decision maker’s own insights…” (Turban, 1995)

23. Spatial Decision Support System Characteristics

24. Current SDSS Maps CMP Map Figures KYOVA Transportation Management Area boundary CMP network Major river crossings Fixed-route transit coverage Computed crash rates compared to statewide average Congested locations from stakeholder workshops Downtown railroad underpass/viaduct locations

25. Current DSS Maps CMP Map FigureTime of DayYears Volumes from Traffic Model Assignments AM Peak, PM Peak, Average Daily Base Year, 2020, 2030, 2040 Capacities from Traffic Model Assignments AM Peak, PM Peak, Average Daily Base Year, 2020, 2030, 2040 V/C Ratios from Traffic Model Assignments AM Peak, PM Peak, Average Daily Base Year, 2020, 2030, 2040 Levels of Service AM Peak, PM Peak, Average Daily Base Year, 2020, 2030, 2040 Travel Time Indices AM Peak, PM Peak, Average Daily Base Year, 2020, 2030, 2040 Planning Time Indices AM Peak, PM Peak, Average Daily Base Year, 2020, 2030, 2040

26. Third Party Data Sources

27. Traffic Conditions Trip Routes Freight Speeds

29. NPMRDS National Performance Measure Research Data Set - 6 th Region

30. West Virginia’s Network

31. November 2014

32. Link Level Speed Data Blue = minimum speeds Red = maximum speeds Arrow = direction of travel

33. Origin-Destination Data

34. Trips by Day Part

35. Tim Padgett, P.E., Kimley-Horn tim.padgett@kimley-horn.com Scott Thomson, P.E., KYTC scott.thomson@ky.gov Saleem Salameh, Ph.D., P.E., KYOVA IPC ssalameh@kyovaipc.org Questions?