Traffic congestion is an escalating concern., to which Traffic Signal Control (TSC) offers a promising solution. While intelligent TSC systems exhibit improved traffic efficiency on static networks, they often struggle to adapt to the dynamic nature of real-world scenarios. characterized by incidents such as acci-dents, road repairs, social events, etc. that disrupt traffic flow. To address this challenge, we propose a Drift-detection Guided Rein-forcement Learning (RL) based TSC (DDGRL-TSC) for dynamic road networks. DDGRL-TSC initiates a virtual RL process, iden-tifies environmental changes, instigates a prioritized sweeping RL process, and then launches a new RL process. This strategy allows for the effective integration of new information into the existing knowledge base, avoiding fixation on previously optimal policies, thereby reducing adaptation time to road network changes and enhancing overall traffic efficiency.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Incremental Reinforcement Learning with Prioritized Sweeping for Traffic Signal Control


    Beteiligte:
    Rattan, Gurseerat Kaur (Autor:in) / Mao, Zhenyu (Autor:in) / Li, Jialong (Autor:in) / Zhang, Mingyue (Autor:in) / Tei, Kenji (Autor:in)


    Erscheinungsdatum :

    28.10.2023


    Format / Umfang :

    1314550 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




    Reinforcement learning-based traffic signal control

    Sheng, Liu Tian / Azman, Afizan Bin / Khan, Navid Ali et al. | IEEE | 2024


    Reinforcement Learning with Explainability for Traffic Signal Control

    Rizzo, Stefano Giovanni / Vantini, Giovanna / Chawla, Sanjay | IEEE | 2019


    Deep Reinforcement Learning-based Traffic Signal Control

    Ruan, Junyun / Tang, Jinzhuo / Gao, Ge et al. | IEEE | 2023