Leveraging Connected Vehicles to Enhance Traffic Responsive Traffic Signal Control

Actuated traffic signal controllers rely on sensors to detect vehicles so that green time can be allocated on a second-by-second basis. Traffic signals that are part of a closed loop system running coordination plans can also utilize detector information to select different pre-programmed plans based on the current traffic state. These Traffic Responsive Plan Selection (TRPS) algorithms currently rely on point detectors that only measure volume and occupancy. With the anticipated implementation of Connected Vehicles, sensors can be installed at signalized intersections to collect the trajectory of these vehicles, which will allow queue lengths to be estimated. Additionally, many radar-based sensors that are currently on the market are capable of tracking vehicles approaching an intersection, which can also be used to estimate queue lengths. This queue length information can be fused with the volume and occupancy data from point detectors to gain an even better understanding of the state of the signal system. This enhanced information could likely allow even better selection of pre-programmed coordination plans. When trajectory-based vehicle information becomes widespread and reliable, it is entirely possible that this information will be used by the controller logic to directly make decisions. In the meantime, this research will investigate whether this information can be leveraged to further enhance TRPS control, which is widely available in most traffic signal controllers. An existing Central system-in-the-loop simulation of a traffic signal system in Morgantown, WV will be utilized to implement and test algorithms for estimating queue lengths from vehicle trajectory data in real-time, estimating the state of the system in real-time, and communicating information back to the controllers to change the timing plans, when appropriate. The advanced TRPS will be compared to basic coordination timing plans and basic TRPS control across various volume scenarios to estimate improvements in delay, emissions, and fuel consumption.

Language

  • English

Project

  • Status: Active
  • Funding: $360000
  • Sponsor Organizations:

    United States Department of Transportation - FHWA - LTAP

    1200 New Jersey Avenue, SE
    Washington, DC    20590

    ECONorthwest

    888 S.W. Fifth, Suite 1460
    Portland, OR  United States  97204-

    Marshall University, Huntington

    College of Information Technology and Engineering
    One John Marshall Drive
    Huntington, WV  United States  25755

    Old Dominion University

    Norfolk, VA  United States  23529

    Virginia Tech Transportation Institute

    3500 Transportation Research Plaza
    Blacksburg, Virginia  United States  24061

    Office of the Assistant Secretary for Research and Technology

    University Transportation Centers Program
    Department of Transportation
    Washington, DC  United States  20590

    Mid-Atlantic Transportation Sustainability Center

    University of Virginia
    Charlottesville, VA  United States 
  • Managing Organizations:

    Mid-Atlantic Transportation Sustainability Center

    University of Virginia
    Charlottesville, VA  United States 
  • Performing Organizations:

    Marshall University, Huntington

    College of Information Technology and Engineering
    One John Marshall Drive
    Huntington, WV  United States  25755

    Old Dominion University

    Norfolk, VA  United States  23529

    Virginia Tech Transportation Institute

    3500 Transportation Research Plaza
    Blacksburg, Virginia  United States  24061
  • Principal Investigators:

    Nichols, Andrew

    Chou, Chih-Sheng

    Cetin, Mecit

    Abbas, Montasir

  • Start Date: 20160501
  • Expected Completion Date: 20171031
  • Actual Completion Date: 0

Subject/Index Terms

Filing Info

  • Accession Number: 01593657
  • Record Type: Research project
  • Source Agency: Mid-Atlantic Transportation Sustainability Center
  • Files: UTC, RiP
  • Created Date: Mar 15 2016 2:35PM