Potential Impact Analysis of Driverless-Cars on Megaregion Traffic Flow Patterns

This study aims to further previous research, GIS-based Megaregion Transportation Planning Model, into the application of self-driving vehicles, developing planning strategy in by sustainable transport system as a series of travel demand modeling. The Year 1 through 3 proposals focused on developing database of freight mobility and identifying its travel patterns. The two-year proposal develops an analytical framework to load not only traditional passenger/freight flows but driverless cars to comprehensively predict the near future travel patterns in Texas mega cities, especially Dallas-Fort Worth and Houston. Its novel approach will help to develop a foundation for promoting sustainable aspect of emerging transportation as a benchmarking tool.

    Language

    • English

    Project

    • Status: Active
    • Funding: $30000
    • Contract Numbers:

      69A3551747135

    • Sponsor Organizations:

      Office of the Assistant Secretary for Research and Technology

      University Transportation Centers Program
      Department of Transportation
      Washington, DC  United States  20590
    • Managing Organizations:

      Office of the Assistant Secretary for Research and Technology

      University Transportation Centers Program
      Department of Transportation
      Washington, DC  United States  20590
    • Project Managers:

      Stearns, Amy

    • Performing Organizations:

      Cooperative Mobility for Competitive Megaregions (CM2)

      University of Texas at Austin
      Austin, TX  United States  78712
    • Principal Investigators:

      Chun, Bumseok

    • Start Date: 20210101
    • Expected Completion Date: 20220731
    • Actual Completion Date: 0
    • USDOT Program: University Transportation Centers Program

    Subject/Index Terms

    Filing Info

    • Accession Number: 01781625
    • Record Type: Research project
    • Source Agency: Cooperative Mobility for Competitive Megaregions (CM2)
    • Contract Numbers: 69A3551747135
    • Files: UTC, RIP
    • Created Date: Sep 15 2021 1:13PM