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Title	Exploring Traffic Signal Control: A Comprehensive Survey On Reinforcement Learning Techniques
ID_Doc	25829
Authors	Thamaraiselvi K.; Bohra A.R.; Vishal V.; Sunkara P.S.; Sunku B.; Nityajignesh B.
Year	2025
Published	3rd IEEE International Conference on Industrial Electronics: Developments and Applications, ICIDeA 2025
DOI	http://dx.doi.org/10.1109/ICIDeA64800.2025.10963194
Abstract	Urban traffic congestion at intersections poses a significant challenge, leading to inefficiencies in traffic flow and increased wait times. Traditional fixed-timer traffic signal systems fail to adapt to fluctuating traffic volumes, exacerbating congestion during peak hours and leading to unnecessary delays during off-peak hours. Reinforcement Learning (RL) offers a promising solution by enabling traffic signal control systems to dynamically adjust signal timings based on real-time data. This paper surveys the current state of traffic signal control systems, highlighting the advantages of RL-based methods and presenting future directions for improvement, including the integration of Internet of Things (IoT) devices, advanced RL algorithms, and enhanced pedestrian detection systems. Through a comparative analysis of existing methods, we propose future enhancements that can further optimize urban traffic management. © 2025 IEEE.
Author Keywords	Adaptive Systems; IoT; Reinforcement Learning; Smart Cities; Traffic Signal Control; Urban Traffic Management

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