Abstract In urban areas, the problem of recurring daily congestion is constantly increasing. A possible solution is seen in the application of adaptive traffic signal control (ATSC) systems for the control of signalized intersections. While available ATSC systems can achieve an increase in the Level of Service, the focus of ATSC research has shifted towards the application of reinforcement learning (RL) techniques, which allow the controller to learn the optimal control policy by direct interaction with the environment. This paper describes the fundamentals of traffic signal control, RL algorithms and approaches and their application to ATSC, with a discussion on the impact of connected and autonomous vehicles on future traffic signal control. In conclusion, a summary of open research questions and possible directions for future research in the domain of RL‐based traffic signal control is given.


    Access

    Download


    Export, share and cite



    Title :

    A review of reinforcement learning applications in adaptive traffic signal control


    Contributors:


    Publication date :

    2022




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    Unknown




    A review of reinforcement learning applications in adaptive traffic signal control

    Miletić, Mladen / Ivanjko, Edouard / Gregurić, Martin et al. | Wiley | 2022

    Free access


    Reinforcement Learning for True Adaptive Traffic Signal Control

    Abdulhai, B. / Pringle, R. / Karakoulas, G. J. | British Library Online Contents | 2003


    An Experimental Review of Reinforcement Learning Algorithms for Adaptive Traffic Signal Control

    Mannion, Patrick / Duggan, Jim / Howley, Enda | Springer Verlag | 2016


    Adaptive traffic signal control method combining traffic flow prediction and reinforcement learning

    PI JIATIAN / YANG XINMIN / WU CHANGZHI | European Patent Office | 2023

    Free access