Leveraging Connected Vehicles to Enhance Traffic Responsive Traffic Signal Control

For traffic signal control, Time of Day (TOD) mode of operations is widely deployed in practice for selecting a signal timing plan. However, TOD mode is not effective in adapting to variations in traffic conditions, such as special events and holidays, incidents, etc. Several research studies have reported the potential of Traffic Responsive Control operation or Traffic Responsive Plan Selection (TRPS) in reducing delays and the number of stops. For successful implementation of TRPS, accurate traffic state estimation is essential. The current study in this direction investigates a methodology for traffic state estimation for a corridor in Morgantown, West Virginia, by using system detector data and connected vehicles (CV) data. Data from CVs form the basis to estimate queue lengths at signalized intersection approaches. While using data from multiple sources, a single measure in terms of three plan selection parameter was obtained, based on which discriminant functions were developed to classify the observations into states. Based on k-means clustering, similar traffic states were grouped together and a new set of states were suggested in place of the original states for which up to 93% classification accuracy was obtained. Overall, it was demonstrated that queue length data can be a valuable source of information for traffic state estimation that is needed for implementing the TRPS framework.

  • Record URL:
  • Record URL:
  • Supplemental Notes:
    • This document was sponsored by the U.S. Department of Transportation, University Transportation Centers Program.
  • Corporate Authors:

    Virginia Polytechnic Institute and State University, Blacksburg

    Blacksburg, VA  United States  24061

    Old Dominion University

    Transportation Research Institute (TRI)
    4111 Monarch Way, Suite 204
    Norfolk, VA  United States  23506

    Marshall University, Huntington

    Huntington, WV  United States  25755

    Mid-Atlantic Transportation Sustainability Center

    University of Virginia
    Charlottesville, VA  United States 

    Office of the Assistant Secretary for Research and Technology

    University Transportation Centers Program
    Department of Transportation
    Washington, DC  United States  20590
  • Authors:
    • Fulari, Shrikant
    • Abbas, Montasir
    • Salahshour, Behrouz
    • Cetin, Mecit
    • Zatar, Wael
    • Nichols, Andrew P
  • Publication Date: 2019-5


  • English

Media Info

  • Media Type: Digital/other
  • Edition: Final Report
  • Features: Appendices; Figures; Maps; References; Tables;
  • Pagination: 40p

Subject/Index Terms

Filing Info

  • Accession Number: 01706817
  • Record Type: Publication
  • Created Date: May 16 2019 12:03PM