Development of Dynamic Network Traffic Simulator for Mixed Traffic Flow Under Connected and Autonomous Vehicles

The advent of connected and autonomous vehicles (CAVs) will generate changes that have the potential to enhance network capacity, reduce congestion, and increase safety. While several studies have examined the potential impact of CAVs on the driving environment, there is the key need for modeling approaches that can characterize network level evolution of traffic flow dynamics and the impacts on stability under mixed traffic streams of human-driven vehicles and CAVs. There is the need for a comprehensive traffic flow modeling framework that incorporates different levels of connectivity and automation as well as different market penetration rates. This study will develop a unified car-following modeling framework that models mixed traffic streams under different market penetration rates of CAVs. It will also perform stability analyses to explore implications for safety and mobility.


  • English


  • Status: Active
  • Contract Numbers:


  • Sponsor Organizations:

    Department of Transportation

    Office of the Secretary
    1200 New Jersey Avenue, S.E.
    Washington, DC    20590
  • Managing Organizations:

    Center for Connected and Automated Transportation

    University of Michigan, Ann Arbor
    Ann Arbor, MI  United States  48109
  • Performing Organizations:

    Purdue University, Lyles School of Civil Engineering

    550 Stadium Mall Drive
    West Lafayette, IN  United States  47907
  • Principal Investigators:

    Peeta, Srinivas

  • Start Date: 20170801
  • Expected Completion Date: 20180731
  • Actual Completion Date: 0
  • USDOT Program: University Transportation Centers Program

Subject/Index Terms

Filing Info

  • Accession Number: 01645396
  • Record Type: Research project
  • Source Agency: Center for Connected and Automated Transportation
  • Contract Numbers: 69A3551747105
  • Files: UTC, RiP
  • Created Date: Aug 31 2017 2:47PM