Traffic problems often occur due to the traffic demands by the outnumbered vehicles on road. Maximizing traffic flow and minimizing the average waiting time are the goals of intelligent traffic control. Each junction wants to get larger traffic flow. During the course, junctions form a policy of coordination as well as constraints for adjacent junctions to maximize their own interests. A good traffic signal timing policy is helpful to solve the problem. However, as there are so many factors that can affect the traffic control model, it is difficult to find the optimal solution. The disability of traffic light controllers to learn from past experiences caused them to be unable to adaptively fit dynamic changes of traffic flow. Considering dynamic characteristics of the actual traffic environment, reinforcement learning algorithm based traffic control approach can be applied to get optimal scheduling policy. The proposed Sarsa(λ)-based real-time traffic control optimization model can maintain the traffic signal timing policy more effectively. The Sarsa(λ)-based model gains traffic cost of the vehicle, which considers delay time, the number of waiting vehicles, and the integrated saturation from its experiences to learn and determine the optimal actions. The experiment results show an inspiring improvement in traffic control, indicating the proposed model is capable of facilitating real-time dynamic traffic control.


    Access

    Download


    Export, share and cite



    Title :

    A Sarsa(λ)-Based Control Model for Real-Time Traffic Light Coordination


    Contributors:
    Xiaoke Zhou (author) / Fei Zhu (author) / Quan Liu (author) / Yuchen Fu (author) / Wei Huang (author)


    Publication date :

    2014




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    Unknown




    SARSA-based trunk line traffic control optimization method

    LIN JING / WEI PING / CHAI ZIHUI et al. | European Patent Office | 2021

    Free access

    Cruise dynamic pricing based on SARSA algorithm

    Wang, Jing / Yang, Dong / Chen, Kaimin et al. | Taylor & Francis Verlag | 2021


    Hierarchical Sarsa Learning Based Route Guidance Algorithm

    Feng Wen / Xingqiao Wang / Xiaowei Xu | DOAJ | 2019

    Free access

    Intelligent Control Strategy of Urban Rail Train Based on Sarsa(λ) Algorithm

    Jiang, Xiaoyi / Shi, Kun / Liu, Yatong et al. | IEEE | 2024


    Central type dynamic path inducing method based on Sarsa learning

    WEN FENG / WANG XINGQIAO / MIAO WEIPING et al. | European Patent Office | 2015

    Free access