Traffic congestion, a major economic and environmental burden, is worsened by inefficient traffic lights leading to higher CO2 emissions. This study explores Deep Q-learning (DQN), a form of reinforcement learning (RL), to optimize traffic light timing and reduce pollution. Using a Barcelona traffic model in SUMO, we demonstrate that DQN-based traffic management can significantly decrease pollution compared to traditional methods. This research highlights the potential of RL for intelligent and sustainable traffic management.


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    Titel :

    Enhancing Urban Pollution Reduction via Reinforcement Learning-Based Traffic Light Optimization


    Weitere Titelangaben:

    Lect. Notes in Networks, Syst.


    Beteiligte:
    Farhaoui, Yousef (Herausgeber:in) / Herawan, Tutut (Herausgeber:in) / Imoize, Agbotiname Lucky (Herausgeber:in) / Allaoui, Ahmad El (Herausgeber:in) / Essamlali, Ismail (Autor:in) / Nhaila, Hasna (Autor:in) / El Khaili, Mohamed (Autor:in)

    Kongress:

    The International Conference on Artificial Intelligence and Smart Environment ; 2024 ; Errachidia, Morocco November 07, 2024 - November 09, 2024



    Erscheinungsdatum :

    03.05.2025


    Format / Umfang :

    7 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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