Active Traffic Signal Control for Mixed Vehicular Traffic in Connected Environments: Self-identifying Platoon Strategy

This study proposes an efficient and generic adaptive signal control algorithm for mixed traffic environments, aimed at improving the quality of service–in terms of minimizing delay and maximizing throughput. An intelligent fully-actuated controller logic computes vehicle-based performance metrics to optimize control parameters in real-time. Connected and automated vehicles (CAVs) are assumed to transmit their accumulated delay to enable vehicle progression. The study incorporates the concept of marginal delay cost in its objective function calculation, i.e. accounts for the unserved vehicles’ extra delay for any additional vehicle served. The proposed method puts forward a control logic that proactively determines the next best phase-platoon to serve and then continually adjusts its phase duration in response to the prevailing demand and flow pattern. An Adjustment Margin is established to account for the position correction of vehicles on an approach, had there been no lead/preceding vehicle. The optimal number of clusters i.e. platoons (per approach) is demand-driven, and therefore independent of any (critical) threshold such as critical inter-vehicle spatial or temporal headway, cumulative headway, (minimum) number of vehicles constituting a platoon, etc. Numerical experiment results show that the control strategy improves the traffic flow by increasing speed, reducing delays and queue length under different mixes of vehicle types.

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

    Transportation Research Board

  • Authors:
  • Conference:
  • Date: 2019


  • English

Media Info

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

Subject/Index Terms

Filing Info

  • Accession Number: 01698316
  • Record Type: Publication
  • Report/Paper Numbers: 19-05931
  • Files: TRIS, TRB, ATRI
  • Created Date: Dec 7 2018 9:52AM