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Developing a Technique that Predicts the Impacts of Transportation Demand Management (TDM) on a Transportation SystemGiven declining resources, pressing problems, and environmental constraints, state departments of transportation (DOTs) are increasingly motivated to manage peak demand of vehicle trips as a way to mitigate congestion and improve overall performance of the highway system. Managing demand this way requires an ability to predict the magnitude and geographic distribution of potential changes in travel behavior resulting from steps taken at specific locations to manage transportation demand. It also requires an ability to build on these predictions to estimate the impacts of implementing transportation demand management (TDM) strategies on travel demand on a specific corridor. The research hypothesis assumed increases in employers’ expenditures for subsidies and incentives, in support of employees commute options program, will decrease the drive-alone rate. If a strong relationship between expenditures and changes in mode split were established, TDM programs can be incorporated more confidently as a congestion management option. The research centered on the relationship between TDM effectiveness at the worksite level and employer-based TDM program costs. Data collection efforts focused on the examination of thousands of employer trip reduction plans submitted over many years and cost survey results of those employers with the goal of integrating the information with the new TDM Assessment Procedure (TDMAP). TDMAP incorporates TDM into the transportation planning process by modifying mode split tables to reflect the impact of implementing a particular TDM strategy or mix of strategies in specific corridors over time. TDMAP consists of a set of subroutines that integrate with NCTR/FDOT-developed TRIMMS© 2.0. Despite using the most comprehensive data set from the State of Washington Commute Trip Reduction program, a strong relationship between changes in expenditures for subsidies and incentives, and changes in the drive-alone rate could not be established. The project concluded with developing a practical guide for collecting TDM cost data at the worksite level and providing an example of the TDMAP.
Download the final report. For more information, contact Nevine Labib Georggi at georggi@cutr.usf.edu, Edward Hillsman at hillsman@cutr.usf.edu, Chanyoung Lee at cylee@cutr.usf.edu, or Philip Winters at winters@cutr.usf.edu.
03.12.10 |
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