Deep Reinforcement Learning-Based Traffic Signal Control for Urban Congestion

Due to limited infrastructure capacity and the increasing number of vehicles, traffic congestion remains a major problem in urban management. The scalability and adaptability issues with traditional traffic signal control (TSC) techniques call for innovative solutions. In order to optimize intelligent transportation systems (ITS), this paper investigates the use of deep reinforcement learning (DRL) models, specifically deep Q-networks (DQN) and their variations. In this study, we employed a Markov Decision Process (MDP) framework to assess how various RL models improve traffic flow efficiency and alleviate congestion. This allows adaptive real-time decision-making to improve traffic flow. We assess how well two RL agents - "Choose Next Phase"and "Next or Not"- manage traffic at a four-way intersection. The simulation results show that RL-based methods perform better than conventional methods, reducing emergency vehicle delays, average waiting time, and queue length. By obtaining higher and more consistent rewards over training iterations, the "Choose Next Phase"agent showed exceptional adaptability. Additionally, under abnormal traffic conditions, we compare RL-based TSC with traditional heuristic-based techniques, emphasizing DRL's superior handling of complex scenarios. To further increase scalability and decision-making reliability in practical applications, future research is highlighted with an emphasis on improving RL models using multi-agent learning, transformer-based architectures, and decentralized computing.