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.


    Access

    Download


    Export, share and cite



    Title :

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


    Contributors:


    Publication date :

    2023




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    Unknown




    Virtual Platoon based CAVs Cooperative Driving at Unsignalized Intersection

    Cong, Xiangyue / Yang, Bo / Gao, Fengkun et al. | IEEE | 2022



    CAVS 2019 Reviewers

    IEEE | 2019

    Free access


    Multi-objective Cooperative Scheduling of CAVs at Non-Signalized Intersection

    Zhao, Xing / Wang, Jinxiang / Chen, Yifeng et al. | IEEE | 2018