Estimating switching times of Actuated Coordinated Traffic Signals: A deep learning approach

Acceleration and Deceleration at signalized intersections are a major hindrance to vehicle fuel efficient operations. Green Light optimal speed advisory (GLOSA) allows controlling vehicles in a fuel-efficient manner but requires reliable estimates of signal switching time. This study aims at utilizing data from actuated coordinated signalized intersections in North Virginia along with multiple deep learning and machine learning techniques to provide estimates of traffic signal switching times from green to red and vice versa. These estimates can be used to enable more fuel-efficient operation using GLOSA and eco-driving. They can also be used to mitigate dilemma zone safety concerns. A comparative analysis will be conducted between the different techniques used and their pros and cons in terms of prediction errors and robustness to different traffic conditions.

Language

  • English

Project

  • Status: Completed
  • Funding: $60000
  • Contract Numbers:

    69A43551747123

  • Sponsor Organizations:

    Office of the Assistant Secretary for Research and Technology

    University Transportation Centers Program
    Department of Transportation
    Washington, DC  United States  20590
  • Managing Organizations:

    Urban Mobility & Equity Center

    Morgan State University
    Baltimore, MD  United States  21251
  • Performing Organizations:

    Virginia Polytechnic Institute and State University, Blacksburg

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

    Rakha, Hesham

    Eteifa, Seifeldeen

    Eldardiry, Hoda

  • Start Date: 20201001
  • Expected Completion Date: 20211231
  • Actual Completion Date: 20211101
  • USDOT Program: University Transportation Centers Program

Subject/Index Terms

Filing Info

  • Accession Number: 01762354
  • Record Type: Research project
  • Source Agency: Urban Mobility & Equity Center
  • Contract Numbers: 69A43551747123
  • Files: UTC, RIP
  • Created Date: Jan 19 2021 2:18PM