Traffic Signal Cycle Control with Centralized Critic and Decentralized Actors under Varying Intervention Frequencies
원문 초록
Traffic congestion in urban areas is a significant problem, leading to prolonged travel times, reduced efficiency, and increased environmental concerns. Effective traffic signal control (TSC) is a key strategy for reducing congestion. Unlike most TSC systems that rely on high-frequency control, this study introduces an innovative joint phase traffic signal cycle control method that operates effectively with varying control intervals. Our method features an adjust all phases action design, enabling simultaneous phase changes within the signal cycle, which fosters both immediate stability and sustained TSC effectiveness, especially at lower frequencies. The approach also integrates decentralized actors to handle the complexity of the action space, with a centralized critic to ensure coordinated phase adjusting. Extensive testing on both synthetic and real-world data across different intersection types and signal setups shows that our method significantly outperforms other popular techniques, particularly at high control intervals. Case studies of policies derived from traffic data further illustrate the robustness and reliability of our proposed method.
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Joint Pedestrian and Vehicle Traffic Optimization in Urban Environments using Reinforcement Learning
A Constrained Multi-Agent Reinforcement Learning Approach to Autonomous Traffic Signal Control
Generalized Phase Pressure Control Enhanced Reinforcement Learning for Traffic Signal Control
A Parallel Hybrid Action Space Reinforcement Learning Model for Real-world Adaptive Traffic Signal Control
CoLLMLight: Cooperative Large Language Model Agents for Network-Wide Traffic Signal Control
MacLight: Multi-scene Aggregation Convolutional Learning for Traffic Signal Control
토론
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