Reinforcement learning for traffic signal control in large-scale transportation networks: a systematic literature review
Effective Traffic Signal Control (TSC) in large-scale transportation networks is essential for enhancing urban mobility, reducing congestion, and improving safety. However, traditional control methods often fail to effectively address the complexity, dynamic conditions, and multimodal demands of modern urban traffic systems. In recent years, Reinforcement Learning (RL) has emerged as a promising solution for achieving adaptive and scalable TSC. This paper presents a systematic and up-to-date review of RL-based methods for large-scale TSC. We analyze representative studies published between 2013 and 2025, presenting a comprehensive analysis of traffic simulation environments, transportation modalities, and advances in methodologies. Key aspects include multi-agent paradigms, state and action representations, reward mechanisms, RL frameworks, as well as advanced representation learning and cooperative strategies for large-scale transportation networks. We also provide a critical discussion on performance evaluation and opportunities for improvement, and conclude by summarizing the current challenges and outlining future research directions. This review aims to inform and guide the development of next-generation RL-based TSC systems that promote sustainable, safe, and efficient urban transportation.