Toward a More Efficient Network Structure for Travel Demand Modeling

The purpose of this research is to examine the issues related to the network structure of travel demand models, and, in particular, of the statewide model, in order to identify a more efficient multi-resolution network structure and a consistent process that will enable effective information sharing with the District's models or local models, and that will optimize model execution, while preserving the detailed attribution provided by the finer network segmentation. This will allow modelers to access information from other models at different geographic scopes and be much more efficient and productive when executing travel demand models, without the barriers of the different network data structures, and while maintaining their model independence. This research will develop a framework to research these goals and explore and test its feasibility for implementation.

    Language

    • English

    Project

    • Status: Active
    • Funding: $250000
    • Contract Numbers:

      BDV31 977-87

    • Sponsor Organizations:

      Florida Department of Transportation

      Research Center
      605 Suwannee Street MS-30
      Tallahassee, FL  United States  32399-0450
    • Project Managers:

      Majano, Vladimir

    • Performing Organizations:

      University of Florida, Gainesville

      219 Grinter Hall
      Gainesville, FL  United States  32611
    • Principal Investigators:

      Bejleri, Ilir

    • Start Date: 20171207
    • Expected Completion Date: 20190630
    • Actual Completion Date: 0

    Subject/Index Terms

    • TRT Terms: Networks; Travel demand
    • Geographic Terms: Florida
    • Subject Areas: Operations and Traffic Management; Planning and Forecasting; Public Transportation;

    Filing Info

    • Accession Number: 01653379
    • Record Type: Research project
    • Source Agency: Florida Department of Transportation
    • Contract Numbers: BDV31 977-87
    • Files: RiP, STATEDOT
    • Created Date: Dec 12 2017 3:06PM