Transition Period from Today to Fully Autonomous

Thanks to innovations from Silicon Valley, what was once thought to be a timeline that would introduce autonomous vehicles by 2035 has turned into a race to produce autonomous vehicles as fast as possible; now, the horizon for commercially available autonomous vehicles appears to be at the beginning of the next decade. However, connected autonomous vehicles (CAVs) will coexist with regular vehicles for several decades, and understanding traffic patterns during the transition period is critical to support planning and operations decisions. While behavioral modeling tools may be used to conduct such an assessment, it is also important to consider that models may require substantial changes in order to capture the impact of CAVs on traffic flow. This research will work to understand the challenges of capturing the impacts of CAVS for different traffic models. Propose adequate methodologies to incorporate CAVs into the traffic models used in this project and estimate model parameters to reflect the impact of CAVs in the selected traffic model.


  • English


  • Status: Completed
  • Contract Numbers:


  • Sponsor Organizations:

    Office of the Assistant Secretary for Research and Technology

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

    Bhat, Chandra

  • Performing Organizations:

    Data-Supported Transportation Operations and Planning Center

    University of Texas at Austin
    Austin, TX  United States  78701
  • Principal Investigators:

    Ruiz Juri, Natalia

  • Start Date: 20170901
  • Expected Completion Date: 20180930
  • Actual Completion Date: 0
  • Source Data: 143

Subject/Index Terms

Filing Info

  • Accession Number: 01634960
  • Record Type: Research project
  • Source Agency: Data-Supported Transportation Operations and Planning Center
  • Contract Numbers: DTRT13-G-UTC58
  • Files: UTC, RIP
  • Created Date: May 18 2017 9:28PM