Papers Traffic Signal Control
“Traffic Signal Control” 태그가 달린 논문 201편 · 필터 해제
HiLight: A Hierarchical Reinforcement Learning Framework with Global Adversarial Guidance for Large-Scale Traffic Signal Control
Efficient traffic signal control (TSC) is essential for mitigating urban congestion, yet existing reinforcement learning (RL) methods face challenges in scaling to large networks while maintaining global coordination. Ce…
Hierarchical Reinforcement Learningreinforcement-learningReinforcement LearningReinforcement Learning (RL)+1Robustness of Reinforcement Learning-Based Traffic Signal Control under Incidents: A Comparative Study
Reinforcement learning-based traffic signal control (RL-TSC) has emerged as a promising approach for improving urban mobility. However, its robustness under real-world disruptions such as traffic incidents remains largel…
BenchmarkingTraffic Signal ControlVLMLight: Traffic Signal Control via Vision-Language Meta-Control and Dual-Branch Reasoning
Traffic signal control (TSC) is a core challenge in urban mobility, where real-time decisions must balance efficiency and safety. Existing methods - ranging from rule-based heuristics to reinforcement learning (RL) - oft…
Large Language ModelReinforcement Learning (RL)Traffic Signal ControlCV-MP: Max-Pressure Control in Heterogeneously Distributed and Partially Connected Vehicle Environments
Max-pressure (MP) control has emerged as a prominent real-time network traffic signal control strategy due to its simplicity, decentralized structure, and theoretical guarantees of network queue stability. Meanwhile, adv…
Traffic Signal ControlDynamic Location Search for Identifying Maximum Weighted Independent Sets in Complex Networks
While Artificial intelligence (AI), including Generative AI, are effective at generating high-quality traffic data and optimization solutions in intelligent transportation systems (ITSs), these techniques often demand si…
Traffic Signal ControlJoint Pedestrian and Vehicle Traffic Optimization in Urban Environments using Reinforcement Learning
Reinforcement learning (RL) holds significant promise for adaptive traffic signal control. While existing RL-based methods demonstrate effectiveness in reducing vehicular congestion, their predominant focus on vehicle-ce…
Reinforcement Learning (RL)Traffic Signal ControlSafe and Efficient Coexistence of Autonomous Vehicles with Human-Driven Traffic at Signalized Intersections
The proliferation of connected and automated vehicles (CAVs) has positioned mixed traffic environments, which encompass both CAVs and human driven vehicles (HDVs), as critical components of emerging mobility systems. Sig…
Autonomous VehiclesTraffic Signal ControlTrajectory PlanningFederated Hierarchical Reinforcement Learning for Adaptive Traffic Signal Control
Multi-agent reinforcement learning (MARL) has shown promise for adaptive traffic signal control (ATSC), enabling multiple intersections to coordinate signal timings in real time. However, in large-scale settings, MARL fa…
Federated LearningHierarchical Reinforcement LearningMulti-agent Reinforcement Learningreinforcement-learning+2A Constrained Multi-Agent Reinforcement Learning Approach to Autonomous Traffic Signal Control
Traffic congestion in modern cities is exacerbated by the limitations of traditional fixed-time traffic signal systems, which fail to adapt to dynamic traffic patterns. Adaptive Traffic Signal Control (ATSC) algorithms h…
FairnessMulti-agent Reinforcement LearningTraffic Signal ControlGeneralized Phase Pressure Control Enhanced Reinforcement Learning for Traffic Signal Control
Appropriate traffic state representation is crucial for learning traffic signal control policies. However, most of the current traffic state representations are heuristically designed, with insufficient theoretical suppo…
Reinforcement Learning (RL)Traffic Signal ControlDomain Adaptation Framework for Turning Movement Count Estimation with Limited Data
Urban transportation networks are vital for the efficient movement of people and goods, necessitating effective traffic management and planning. An integral part of traffic management is understanding the turning movemen…
Domain AdaptationManagementTraffic Signal ControlA Parallel Hybrid Action Space Reinforcement Learning Model for Real-world Adaptive Traffic Signal Control
Adaptive traffic signal control (ATSC) can effectively reduce vehicle travel times by dynamically adjusting signal timings but poses a critical challenge in real-world scenarios due to the complexity of real-time decisio…
Decision MakingSequential Decision MakingTraffic Signal ControlUnicorn: A Universal and Collaborative Reinforcement Learning Approach Towards Generalizable Network-Wide Traffic Signal Control
Adaptive traffic signal control (ATSC) is crucial in reducing congestion, maximizing throughput, and improving mobility in rapidly growing urban areas. Recent advancements in parameter-sharing multi-agent reinforcement l…
Contrastive LearningMulti-agent Reinforcement LearningTraffic Signal ControlVariational InferenceCoLLMLight: Cooperative Large Language Model Agents for Network-Wide Traffic Signal Control
Traffic Signal Control (TSC) plays a critical role in urban traffic management by optimizing traffic flow and mitigating congestion. While Large Language Models (LLMs) have recently emerged as promising tools for TSC due…
Computational EfficiencyLanguage ModelingLanguage ModellingLarge Language Model+1Adaptive model predictive control for traffic signal timing with unknown demand and parameters
This paper designs traffic signal control policies for a network of signalized intersections without knowing the demand and parameters. Within a model predictive control (MPC) framework, control policies consist of an al…
Model Predictive ControlTraffic Signal ControlLarge-scale Regional Traffic Signal Control Based on Single-Agent Reinforcement Learning
In the context of global urbanization and motorization, traffic congestion has become a significant issue, severely affecting the quality of life, environment, and economy. This paper puts forward a single-agent reinforc…
Reinforcement Learning (RL)Traffic Signal ControlEnhancing Traffic Signal Control through Model-based Reinforcement Learning and Policy Reuse
Multi-agent reinforcement learning (MARL) has shown significant potential in traffic signal control (TSC). However, current MARL-based methods often suffer from insufficient generalization due to the fixed traffic patter…
Model-based Reinforcement LearningMulti-agent Reinforcement Learningreinforcement-learningReinforcement Learning+1Using a single actor to output personalized policy for different intersections
Recently, with the development of Multi-agent reinforcement learning (MARL), adaptive traffic signal control (ATSC) has achieved satisfactory results. In traffic scenarios with multiple intersections, MARL treats each in…
Graph AttentionMulti-agent Reinforcement LearningTraffic Signal ControlDreamerV3 for Traffic Signal Control: Hyperparameter Tuning and Performance
Reinforcement learning (RL) has evolved into a widely investigated technology for the development of smart TSC strategies. However, current RL algorithms necessitate excessive interaction with the environment to learn ef…
Reinforcement Learning (RL)Traffic Signal ControlToward Dependency Dynamics in Multi-Agent Reinforcement Learning for Traffic Signal Control
Reinforcement learning (RL) emerges as a promising data-driven approach for adaptive traffic signal control (ATSC) in complex urban traffic networks, with deep neural networks substantially augmenting its learning capabi…
Multi-agent Reinforcement LearningReinforcement Learning (RL)Traffic Signal Control