TL;DR
Simulation results in the SUMO environment demonstrate that DDQNTSCA achieves faster convergence, enhanced adaptability, and significant reductions in average travel time, queue length, and cumulative delay compared to existing DRL-based TSC methods.
Simulation results indicate that the proposed Adaptive Quad-Agent Double Deep Q-Network model effectively supports dynamic signal phase adaptation, minimizes congestion, and provides more accurate queue length estimations under complex traffic conditions.
The findings demonstrate the potential of DRL-based traffic signal control in controlled simulation conditions and highlight that algorithm performance is strongly influenced by traffic policy design and environmental complexity.
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.
A novel cooperative MARL-based approach for adaptive traffic signal control in multi-intersection networks that significantly outperforms existing methods in relation to average pheromone intensity, average noise emission, and average waiting time is proposed.
The study uses a dynamic routing framework to create adaptive routing strategies, centered on a customized deep Q-network, that works well in variable traffic scenarios and effectively adapts to peak and off-peak delivery windows.