Papers Traffic Signal Control
“Traffic Signal Control” 태그가 달린 논문 201편 · 필터 해제
Communication Strategy on Macro-and-Micro Traffic State in Cooperative Deep Reinforcement Learning for Regional Traffic Signal Control
Adaptive Traffic Signal Control (ATSC) has become a popular research topic in intelligent transportation systems. Regional Traffic Signal Control (RTSC) using the Multi-agent Deep Reinforcement Learning (MADRL) technique…
Deep Reinforcement LearningTraffic Signal ControlFitLight: Federated Imitation Learning for Plug-and-Play Autonomous Traffic Signal Control
Although Reinforcement Learning (RL)-based Traffic Signal Control (TSC) methods have been extensively studied, their practical applications still raise some serious issues such as high learning cost and poor generalizabi…
Imitation Learningreinforcement-learningReinforcement LearningReinforcement Learning (RL)+1FuzzyLight: A Robust Two-Stage Fuzzy Approach for Traffic Signal Control Works in Real Cities
Effective traffic signal control (TSC) is crucial in mitigating urban congestion and reducing emissions. Recently, reinforcement learning (RL) has been the research trend for TSC. However, existing RL algorithms face sev…
compressed sensingReinforcement Learning (RL)Traffic Signal ControlAMM: Adaptive Modularized Reinforcement Model for Multi-city Traffic Signal Control
Traffic signal control (TSC) is an important and widely studied direction. Recently, reinforcement learning (RL) methods have been used to solve TSC problems and achieve superior performance over conventional TSC methods…
Domain AdaptationMeta-LearningReinforcement Learning (RL)Traffic Signal ControlIntegrated Strategy for Urban Traffic Optimization: Prediction, Adaptive Signal Control, and Distributed Communication via Messaging
This work introduces an integrated approach to optimizing urban traffic by combining predictive modeling of vehicle flow, adaptive traffic signal control, and a modular integration architecture through distributed messag…
Traffic Signal ControlGoal-Conditioned Data Augmentation for Offline Reinforcement Learning
Offline reinforcement learning (RL) enables policy learning from pre-collected offline datasets, relaxing the need to interact directly with the environment. However, limited by the quality of offline datasets, it genera…
D4RLData AugmentationOffline RLreinforcement-learning+3MacLight: Multi-scene Aggregation Convolutional Learning for Traffic Signal Control
Reinforcement learning methods have proposed promising traffic signal control policy that can be trained on large road networks. Current SOTA methods model road networks as topological graph structures, incorporate graph…
Graph AttentionQ-LearningTraffic Signal ControlBayesian Critique-Tune-Based Reinforcement Learning with Adaptive Pressure for Multi-Intersection Traffic Signal Control
Adaptive Traffic Signal Control (ATSC) system is a critical component of intelligent transportation, with the capability to significantly alleviate urban traffic congestion. Although reinforcement learning (RL)-based met…
Bayesian Inferencereinforcement-learningReinforcement LearningReinforcement Learning (RL)+1Artificial Intelligence in Traffic Systems
Existing research on AI-based traffic management systems, utilizing techniques such as fuzzy logic, reinforcement learning, deep neural networks, and evolutionary algorithms, demonstrates the potential of AI to transform…
Autonomous VehiclesEvolutionary AlgorithmsManagementTraffic Signal ControlData-Driven Transfer Learning Framework for Estimating Turning Movement Counts
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…
ManagementTraffic Signal ControlTransfer LearningTransferLight: Zero-Shot Traffic Signal Control on any Road-Network
Traffic signal control plays a crucial role in urban mobility. However, existing methods often struggle to generalize beyond their training environments to unseen scenarios with varying traffic dynamics. We present Trans…
Graph Neural NetworkTraffic Signal ControlIntersection-Aware Assessment of EMS Accessibility in NYC: A Data-Driven Approach
Emergency response times are critical in densely populated urban environments like New York City (NYC), where traffic congestion significantly impedes emergency vehicle (EMV) mobility. This study introduces an intersecti…
Multi-agent Reinforcement LearningTraffic Signal ControlTraffic Co-Simulation Framework Empowered by Infrastructure Camera Sensing and Reinforcement Learning
Traffic simulations are commonly used to optimize traffic flow, with reinforcement learning (RL) showing promising potential for automated traffic signal control. Multi-agent reinforcement learning (MARL) is particularly…
Multi-agent Reinforcement Learningreinforcement-learningReinforcement LearningReinforcement Learning (RL)+2A CAV-based perimeter-free regional traffic control strategy utilizing existing parking infrastructure
This paper proposes a novel perimeter-free regional traffic management strategy for traffic networks under a connected and autonomous vehicle (CAV) environment. The proposed strategy requires CAVs, especially those with …
Traffic Signal ControlIntegrating Transit Signal Priority into Multi-Agent Reinforcement Learning based Traffic Signal Control
This study integrates Transit Signal Priority (TSP) into multi-agent reinforcement learning (MARL) based traffic signal control. The first part of the study develops adaptive signal control based on MARL for a pair of co…
Multi-agent Reinforcement LearningTraffic Signal ControlOptimizing Traffic Signal Control using High-Dimensional State Representation and Efficient Deep Reinforcement Learning
In reinforcement learning-based (RL-based) traffic signal control (TSC), decisions on the signal timing are made based on the available information on vehicles at a road intersection. This forms the state representation …
Deep Reinforcement LearningModel CompressionTraffic Signal ControlOffLight: An Offline Multi-Agent Reinforcement Learning Framework for Traffic Signal Control
Efficient traffic control (TSC) is essential for urban mobility, but traditional systems struggle to handle the complexity of real-world traffic. Multi-agent Reinforcement Learning (MARL) offers adaptive solutions, but o…
Multi-agent Reinforcement LearningOffline RLreinforcement-learningReinforcement Learning+1Multi-hop Upstream Anticipatory Traffic Signal Control with Deep Reinforcement Learning
Coordination in traffic signal control is crucial for managing congestion in urban networks. Existing pressure-based control methods focus only on immediate upstream links, leading to suboptimal green time allocation and…
Deep Reinforcement Learningreinforcement-learningReinforcement LearningTraffic Signal ControlDiffLight: A Partial Rewards Conditioned Diffusion Model for Traffic Signal Control with Missing Data
The application of reinforcement learning in traffic signal control (TSC) has been extensively researched and yielded notable achievements. However, most existing works for TSC assume that traffic data from all surroundi…
Decision MakingImputationTraffic Data ImputationTraffic Signal ControlPyTSC: A Unified Platform for Multi-Agent Reinforcement Learning in Traffic Signal Control
Multi-Agent Reinforcement Learning (MARL) presents a promising approach for addressing the complexity of Traffic Signal Control (TSC) in urban environments. However, existing platforms for MARL-based TSC research face ch…
ManagementMulti-agent Reinforcement LearningTraffic Signal Control