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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    Title :

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


    Additional title:

    Lect. Notes in Networks, Syst.



    Conference:

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



    Publication date :

    2025-05-03


    Size :

    7 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




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