Multimodal traffic light control is a cost-effective way to deal with urban congestion. The development of V2X (Vehicle to Everything) technologies offers unprecedented data and hence new opportunities for situation awareness, but the conventional control algorithms fall short of fully exploiting the real-time vehicle information at the intersections. In this work, a Double Deep Q-learning (DDQL) approach is proposed for multimodal traffic light control with different priority requests in a connected vehicle environment. The proposed DDQL approach is integrated with the existing actuated controller and is readily implementable. The integrated system can terminate the DDQL controller and switch to the actuated controller for safety when an urgent issue occurs such as an electric power outage. The simulation results demonstrate the advantage of the proposed approach compared with actuated control and indicate the reduction of delays for both public transportation and personal vehicle by the proposed approach.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Multimodal Traffic Light Control with Connected Vehicles: A Deep Reinforcement Learning Approach


    Contributors:


    Publication date :

    2023-06-14


    Size :

    2672706 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English






    Lateral flow control of connected vehicles through deep reinforcement learning

    Kreidieh, Abdul Rahman / Farid, Yashar / Oguchi, Kentaro | IEEE | 2023


    Deep reinforcement learning traffic light control method

    KONG YAN / LI YING / CHIH-CHAO YANG | European Patent Office | 2024

    Free access