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.
Enhancing Urban Pollution Reduction via Reinforcement Learning-Based Traffic Light Optimization
Lect. Notes in Networks, Syst.
The International Conference on Artificial Intelligence and Smart Environment ; 2024 ; Errachidia, Morocco November 07, 2024 - November 09, 2024
Intersection of Artificial Intelligence, Data Science, and Cutting-Edge Technologies: From Concepts to Applications in Smart Environment ; Chapter : 36 ; 269-275
2025-05-03
7 pages
Article/Chapter (Book)
Electronic Resource
English
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