Modeling Cooperative Adaptive Cruise Control in Dynamic Traffic Assignment

Advances in connected and automated vehicle technologies have resulted in new vehicle applications, such as cooperative adaptive cruise control (CACC). Studies have shown significant increases in capacity and stability due to CACC, but most previous work has relied on microsimulation — ignoring route choice and demand impacts. To study the effects of CACC on larger networks and with user equilibrium route choice, the authors incorporate CACC into the link transmission model (LTM) for dynamic network loading. First, the authors derive the flow-density relationship from the MIXIC car-following model of CACC. The flow-density relationship has an unusual shape; part of the congested regime has an infinite congested wave speed. However, the authors verify that the flow predictions match observations from MIXIC modeled in VISSIM. Then, they use the flow- density relationship from MIXIC in LTM. Although the independence of separate links restricts the maximum congested wave speed, for common freeway link lengths the congested wave speed is sufficiently high to fit the observed flows from MIXIC. Results on a large freeway corridor indicate that CACC could significantly reduce travel times over current conditions.

  • Supplemental Notes:
    • This paper was sponsored by TRB committee ADB30 Standing Committee on Transportation Network Modeling.
  • Corporate Authors:

    Transportation Research Board

    500 Fifth Street, NW
    Washington, DC  United States  20001
  • Authors:
    • Melson, Christopher L
    • Levin, Michael W
    • Boyles, Stephen D
  • Conference:
  • Date: 2017

Language

  • English

Media Info

  • Media Type: Digital/other
  • Features: Figures; References;
  • Pagination: 16p
  • Monograph Title: TRB 96th Annual Meeting Compendium of Papers

Subject/Index Terms

Filing Info

  • Accession Number: 01628738
  • Record Type: Publication
  • Report/Paper Numbers: 17-05626
  • Files: TRIS, TRB, ATRI
  • Created Date: Mar 10 2017 9:21AM