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Title	A Reinforcement Learning Approach For Reducing Traffic Congestion Using Deep Q Learning
ID_Doc	4032
Authors	Swapno S.M.M.R.; Nobel S.M.N.; Meena P.; Meena V.P.; Azar A.T.; Haider Z.; Tounsi M.
Year	2024
Published	Scientific Reports, 14, 1
DOI	http://dx.doi.org/10.1038/s41598-024-75638-0
Abstract	Nowadays, traffic congestion is a significant issue globally. The vehicle quantity has grown dramatically, while road and transportation infrastructure capacities have yet to expand proportionally to handle the additional traffic effectively. Road congestion and traffic-related pollution have increased, which is detrimental to society and public health. This paper proposes a novel reinforcement learning (RL)-based method to reduce traffic congestion. We have developed a sophisticated Deep Q-Network (DQN) and integrated it smoothly into our system. In this study, Our implemented DQL model reduced queue lengths by 49% and increased incentives for each lane by 9%. The results emphasize the effectiveness of our method in setting strong traffic reduction standards. This study shows that RL has excellent potential to improve both transport efficiency and sustainability in metropolitan areas. Moreover, utilizing RL can significantly improve the standards for reducing traffic and easing urban traffic congestion. © The Author(s) 2024.
Author Keywords	Agent; DQL; Intersection; Queue length; Rewards; RL; Smart city; Traffic reduction

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