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

“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

Forecasting Background Why do we use forecasting models?

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

2015 KYOVA Travel Demand Model

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”

What’s Different Boundary Capacity methodology

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

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)

Additional Features Free-flow speed override Simplified user interface

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

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 daily, hourly, and truck counts for calibration and validation Trip distribution -Re-estimate gravity model parameters to reflect expanded area

Challenges and Successes Challenges Data Multiple jurisdictions Successes Coordination and cooperation

Thinking Beyond the Model

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

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?

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

Mode Share

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

KYOVA’s Spatial Decision Support System (SDSS) Integration

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)

Spatial Decision Support System Characteristics

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

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

Third Party Data Sources

Traffic Conditions Trip Routes Freight Speeds

NPMRDS National Performance Measure Research Data Set - 6 th Region

West Virginia’s Network

November 2014

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

Origin-Destination Data

Trips by Day Part

Tim Padgett, P.E., Kimley-Horn Scott Thomson, P.E., KYTC Saleem Salameh, Ph.D., P.E., KYOVA IPC Questions?