A Real-Time Network-Level Traffic Signal Control Methodology With Partial Vehicle Information

This study presents a distributed formulation and a real-time methodology for traffic signal control in urban-street networks for connected vehicle (CV) with various market penetration rates. The proposed distributed model predictive control (DMPC) estimates the state of the network by integrating CV and point detector data, optimizes the signal timing parameters over a prediction period constituting several time steps, implements the optimal decisions in the next time step, and continues this process until the end of the study period. This paper proposes two algorithms to estimate the network state in an environment with partial information: The first algorithm uses a car following model to predict the position of unequipped vehicles based on leader-follower relationship in a link after their detection on a point detector. The second algorithm converts the temporal vehicle detection distribution to a spatial vehicle distribution on a link. The proposed DMPC is applied to a real-world case study network simulated in Vissim. The results show that a both traffic state estimation algorithms are effective under all CV market penetration rates: at 0% CV penetration, the proposed methodology reduced travel time by 21% to 27% and average delay by 30% to 41% compared to the existing signal timing parameters. At 40% CV penetration, the proposed algorithm reduced travel time by 28% to 31% and average delay by 43% to 49% compared to the existing signal timing parameters.

  • Supplemental Notes:
    • This paper was sponsored by TRB committee AHB25 Standing Committee on Traffic Signal Systems.
  • Corporate Authors:

    Transportation Research Board

  • Authors:
    • Bin Al Islam, S M A
    • Hajbabaie, Ali
    • Aziz, H M Abdul
  • Conference:
  • Date: 2019


  • English

Media Info

  • Media Type: Digital/other
  • Features: Figures; References; Tables;
  • Pagination: 6p

Subject/Index Terms

Filing Info

  • Accession Number: 01698309
  • Record Type: Publication
  • Report/Paper Numbers: 19-02829
  • Files: TRIS, TRB, ATRI
  • Created Date: Dec 7 2018 9:51AM