Traffic congestion has become an increasingly concerning problem in modern society. Recent research has proven that Reinforcement Learning (RL) applied to Traffic Signal Control (TSC) is useful in mitigating congestion. In this paper, a model of real-world intersection with real traffic data collected in Hangzhou, China is simulated with different RL based traffic signal controllers. Two model free reinforcement learning methods are proposed namely: Deep Q-Learning (DQN) and double DQN (DDQN). These models are trained and tested at a 4-way intersection. Model adaptability and performance in different traffic scenarios are also measured and discussed in this paper.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Deep Reinforcement Learning-based Traffic Signal Control


    Beteiligte:
    Ruan, Junyun (Autor:in) / Tang, Jinzhuo (Autor:in) / Gao, Ge (Autor:in) / Shi, Tianyu (Autor:in) / Khamis, Alaa (Autor:in)


    Erscheinungsdatum :

    2023-03-19


    Format / Umfang :

    1287120 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Traffic signal control method based on deep reinforcement learning

    LIU DUANYANG / SHEN SI / SHEN GUOJIANG et al. | Europäisches Patentamt | 2021

    Freier Zugriff

    Applications on Deep Reinforcement Learning in Traffic Signal Control

    Sun, Haolun / Sun, Yilong / Yu, Boyang | IEEE | 2022


    A Deep Reinforcement Learning Approach to Traffic Signal Control

    Razack, Aquib Junaid / Ajith, Vysyakh / Gupta, Rajiv | IEEE | 2021


    Multi-intersection traffic signal control method based on deep reinforcement learning

    LIU LIJUAN / BAI GUANGMING | Europäisches Patentamt | 2023

    Freier Zugriff

    Deep reinforcement learning traffic signal control method based on attention mechanism

    WU JIANGUANG / ZHOU SHUYA / HOU XIANGDONG et al. | Europäisches Patentamt | 2024

    Freier Zugriff