Integrated Travel Demand Model
Challenges and Successes
Tim Padgett, P.E., Kimley-HornScott Thomson, P.E., KYTC
Saleem Salameh, Ph.D., P.E., KYOVA IPC
2015 WV Planning ConferenceSeptember 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”
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
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- 2010 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
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 Figure Time of Day Years
Volumes from Traffic Model AssignmentsAM Peak, PM Peak, Average Daily
Base Year, 2020, 2030, 2040
Capacities from Traffic Model AssignmentsAM Peak, PM Peak, Average Daily
Base Year, 2020, 2030, 2040
V/C Ratios from Traffic Model AssignmentsAM Peak, PM Peak, Average Daily
Base Year, 2020, 2030, 2040
Levels of ServiceAM Peak, PM Peak, Average Daily
Base Year, 2020, 2030, 2040
Travel Time IndicesAM Peak, PM Peak, Average Daily
Base Year, 2020, 2030, 2040
Planning Time IndicesAM Peak, PM Peak, Average Daily
Base Year, 2020, 2030, 2040
Third Party Data Sources
Traffic Conditions
Trip RoutesFreight Speeds
NPMRDS
National Performance Measure Research Data Set - 6th 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., [email protected]
Scott Thomson, P.E., [email protected]
Saleem Salameh, Ph.D., P.E., KYOVA [email protected]
Questions?