Connected Vehicle Based Dynamic All-Red Extension for Adaptive Signalized Intersections

This paper proposes a connected vehicle (CV) based dynamic all-red extension (DARE) framework for adaptive signalized intersections to avoid potential crashes caused by red-light running (RLR) behaviors. The conceptual structure consists of three components, i.e. CVs, road-side equipment (RSE), traffic control devices. Under the CV environment, vehicle trajectories and continuous vehicle running information could be obtained via vehicle-to-infrastructure and vehicle-to-vehicle communications. Under such advanced detection information for vehicles that approach to a signalized intersection, a mathematic model is built to predict the potential of RLR. As a comparison, the current approach of DARE at adaptive signalized intersections based on inductive loop detectors is presented as well. In the field experiments, vehicle trajectories collected by radar sensors were used to simulate the CV environment. Based on empirical measurements, results in terms of prediction rate, missing rate and false alarm rate show the merits of CV continuous trajectories in RLR prediction accuracy improvement, and thus to improve intersection safety.

  • Availability:
  • Supplemental Notes:
    • Abstract used with permission of ITS Japan. Paper No. 4150.
  • Corporate Authors:

    ITS Japan

    Tokyo,   Japan 
  • Authors:
    • Chen, Xiqun Michael
    • Xu, Dingyuan
    • Lin, Xi
    • Cui, Xiaojie
    • Sun, Ting
    • Li, Meng
  • Conference:
  • Publication Date: 2013


  • English

Media Info

  • Media Type: Digital/other
  • Features: Figures; Photos; References;
  • Pagination: 12p
  • Monograph Title: 20th ITS World Congress, Tokyo 2013. Proceedings

Subject/Index Terms

Filing Info

  • Accession Number: 01535482
  • Record Type: Publication
  • ISBN: 9784990493981
  • Files: TRIS
  • Created Date: Aug 27 2014 10:47AM