Robust Single-Agent Reinforcement Learning for Regional Traffic Signal Control Under Demand Fluctuations
Qiang Li, Jin Niu, Lina Yu
备注：
A critical error in the methodology. The reported congestion control effects were not caused by the proposed signal timing optimization, but by an incorrect traffic volume scaling factor during evaluation. The traffic demand was not properly amplified, resulting in misleading performance gains. Due to the substantial nature of the error, completion of revisions is not feasible in the short term
Traffic congestion, primarily driven by intersection queuing, significantly impacts urban living standards, safety, environmental quality, and economic efficiency. While Traffic Signal Control (TSC) systems hold potential for congestion mitigation, traditional optimization models often fail to capture real-world traffic complexity and dynamics. This study introduces a novel single-agent reinforcement learning (RL) framework for regional adaptive TSC, circumventing the coordination complexities inherent in multi-agent systems through a centralized decision-making paradigm. The model employs an adjacency matrix to unify the encoding of road network topology, real-time queue states derived from probe vehicle data, and current signal timing parameters. Leveraging the efficient learning capabilities of the DreamerV3 world model, the agent learns control policies where actions sequentially select intersections and adjust their signal phase splits to regulate traffic inflow/outflow, analogous to a feedback control system. Reward design prioritizes queue dissipation, directly linking congestion metrics (queue length) to control actions. Simulation experiments conducted in SUMO demonstrate the model's effectiveness: under inference scenarios with multi-level (10%, 20%, 30%) Origin-Destination (OD) demand fluctuations, the framework exhibits robust anti-fluctuation capability and significantly reduces queue lengths. This work establishes a new paradigm for intelligent traffic control compatible with probe vehicle technology. Future research will focus on enhancing practical applicability by incorporating stochastic OD demand fluctuations during training and exploring regional optimization mechanisms for contingency events.
Communication Strategy on Macro-and-Micro Traffic State in Cooperative Deep Reinforcement Learning for Regional Traffic Signal Control
School of Advanced Technology, Xi’an Jiaotong-Liverpool University（西交利物浦大学先进技术学院）；University of Liverpool（利物浦大学）；Department of Engineering and Design, University of Sussex（萨塞克斯大学工程与设计系）；International Business School Suzhou, Xi’an Jiaotong-Liverpool University（西交利物浦大学苏州国际商学院）；Xidian University（西安电子科技大学）；Department of Computer Science, City University of Hong Kong（香港城市大学计算机科学系）；City University of Hong Kong Shenzhen Research Institute（香港城市大学深圳研究院）
针对区域交通信号控制中的智能体协作问题，提出基于 GAT 聚合的 GA2-Naive 与 GA2-Aug 通信模块，融合宏观/微观及区域内/间交通状态关联，并验证其可稳定提升现有框架性能。
Large-Scale Traffic Signal Control Using Constrained Network Partition and Adaptive Deep Reinforcement Learning
Xi’an Jiaotong-Liverpool University（西交利物浦大学）；University of Liverpool（利物浦大学）；Northeastern University（东北大学）；Xidian University（西安电子科技大学）；The University of Hong Kong（香港大学）
Enhancing Demand-Oriented Regionalization with Agentic AI and Local Heterogeneous Data for Adaptation Planning
College of Design, Construction and Planning, University of Florida（佛罗里达大学设计、建设与规划学院）；Department of Electrical and Computer Engineering, University of Florida（佛罗里达大学电气与计算机工程系）；Florida Institute for Built Environment Resilience (FIBER), University of Florida（佛罗里达大学环境韧性研究所）
Expression might be enough: representing pressure and demand for reinforcement learning based traffic signal control
Lanzhou University（兰州大学）；University of Electronic Science and Technology of China（电子科技大学）；University of Wollongong（伍伦贡大学）；Jiangxi University of Science and Technology（江西理工大学）
Graph Convolutional Network With Pattern-Spatial Interactive and Regional Awareness for Traffic Forecasting
College of Mechanical and Electrical Engineering, Shihezi University（石河子大学机械与电子工程学院）；School of Mathematics and Computational Science, Xiangtan University（湘潭大学数学与计算机科学学院）；College of Sciences, Shihezi University（石河子大学科学学院）
A Knowledge-Guided Cross-Modal Feature Fusion Model for Local Traffic Demand Prediction
Shandong University（山东大学）；Southern University of Science and Technology（南方科技大学）；Northwest University（西北大学）；Didi Chuxing（滴滴出行）；Renmin University of China（中国人民大学）；Jilin University（吉林大学）
News and Load: Social and Economic Drivers of Regional Multi-horizon Electricity Demand Forecasting
Centre for Processes, Renewable Energies and Energy Systems (PERSEE), MINES Paris - PSL University, Sophia Antipolis, France（进程、可再生能源与能源系统中心（PERSEE），MINES巴黎-PSL大学，法国索菲亚-安蒂波利斯）
A physics-informed and attention-based graph learning approach for regional electric vehicle charging demand prediction
School of Intelligent Systems Engineering, Sun Yat-Sen University（中山大学智能工程学院）；Department of Computing, The Hong Kong Polytechnic University（香港理工大学计算学系）
Learning from Yesterday's Error: An Efficient Online Learning Method for Traffic Demand Prediction
Key Laboratory of Road and Traffic Engineering, Ministry of Education at Tongji University（交通工程教育部重点实验室，同济大学）；Urban Mobility Institute, Tongji University（交通 mobility 院，同济大学）
CONTINA: Confidence Interval for Traffic Demand Prediction with Coverage Guarantee
Key Laboratory of Road and Traffic Engineering, Ministry of Education at Tongji University（同济大学交通工程教育重点实验室）；Urban Mobility Institute, Tongji University（同济大学城市交通研究院）
Short-Term Regional Electricity Demand Forecasting in Argentina Using LSTM Networks
Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Instituto de Investigaciones en Fisicoquímica de Córdoba (INFIQC), Universidad Nacional de Córdoba, Facultad de Ciencias Químicas, Departamento de Química Teórica y Computacional（阿根廷国家科学研究与技术理事会（CONICET）、科里亚物理化学研究所（INFIQC）、科里亚国立大学、化学科学学院、理论与计算化学系）
AI-Enhanced Spatial Cellular Traffic Demand Prediction with Contextual Clustering and Error Correction for 5G/6G Planning
Innovation, Science and Economic Development Canada (ISED) and Carleton University（创新、科学与经济发展的加拿大（ISED）和卡尔顿大学）；Communications Research Centre (CRC)（通讯研究中心（CRC））；Carleton University（卡尔顿大学）
