Development of a Signal-Free Intersection Control System for CAVs and Corridor Level Impact Assessment

Ardeshir Mirbakhsh, Joyoung Lee, Dejan Besenski

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


Assuming a full market penetration rate of connected and autonomous vehicles (CAVs) would provide an opportunity to remove costly and inefficient traffic lights from intersections, this paper presents a signal-free intersection control system relying on CAVs’ communicability. This method deploys a deep reinforcement learning algorithm and pixel reservation logic to avoid potential collisions and minimize the overall delay at the intersection. To facilitate a traffic-oriented assessment of the model, the proposed model’s application is coupled with VISSIM traffic microsimulation software, and its performance is compared with other intersection control systems, including fixed traffic lights, actuated traffic lights, and the Longest Queue First (LQF) control system. The simulation result revealed that the proposed model reduces delay by 50%, 29%, and 23% in moderate, high, and extreme volume regimes, respectively, compared to another signal-free control system. Noticeable improvements are also gained in travel time, fuel consumption, emission, and Surrogate Safety Measures.

Original languageEnglish (US)
Pages (from-to)552-567
Number of pages16
JournalFuture Transportation
Issue number2
StatePublished - Jun 2023

All Science Journal Classification (ASJC) codes

  • Engineering (miscellaneous)
  • Renewable Energy, Sustainability and the Environment
  • Energy (miscellaneous)


  • connected and autonomous vehicles
  • deep queue networks
  • deep reinforcement learning
  • machine learning
  • pixel reservation control system
  • signal-free intersection control systems


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