Traffic Signal Control via Reinforcement Learning: A Review on Applications and Innovations

Traffic signal control plays a pivotal role in intelligent transportation systems, directly affecting urban mobility, congestion mitigation, and environmental sustainability. As traffic networks become more dynamic and complex, traditional strategies such as fixed-time and actuated control increasingly fall short in addressing real-time variability. In response, adaptive signal control—powered predominantly by reinforcement learning—has emerged as a promising data-driven solution for optimizing signal operations in evolving traffic environments. The current review presents a comprehensive analysis of high-impact reinforcement-learning-based traffic signal control methods, evaluating their contributions across…