时空双阶段超图多智能体强化学习用于以人为中心的多模式走廊交通信号控制
Spatio-temporal dual-stage hypergraph MARL for human-centric multimodal corridor traffic signal control
AI总结：
本文提出STDSH-MARL方法，通过时空双阶段超图注意力机制和混合动作空间，提升走廊交通信号对多模式旅行者和公共交通的适应性与优先级。
摘要：
走廊网络中的人本交通信号控制必须越来越多地考虑多模式旅行者，特别是高占用率的公共交通，而不仅仅是聚焦于车辆中心的性能。本文提出了STDSH-MARL（基于时空双阶段超图的多智能体强化学习），一种可扩展的多智能体深度强化学习框架，遵循集中训练和分散执行范式。所提出的方法通过一种新颖的双阶段超图注意力机制捕捉时空依赖性，该机制建模了空间和时间超边之间的交互。此外，引入了一种混合离散动作空间，以联合确定下一个信号阶段配置及其对应的绿灯持续时间，使信号定时决策更加适应。在五个交通场景下的走廊网络实验中，STDSH-MARL一致提高了多模式性能，并为公共交通优先提供了明显的好处。与最先进的基线方法相比，所提出的方法实现了总体性能的优越表现。进一步的消融研究证实了STDSH-MARL各组件的贡献，其中时间超边被识别为最影响观察到性能提升的因素。
英文摘要：
Human-centric traffic signal control in corridor networks must increasingly account for multimodal travelers, particularly high-occupancy public transportation, rather than focusing solely on vehicle-centric performance. This paper proposes STDSH-MARL (Spatio-Temporal Dual-Stage Hypergraph based Multi-Agent Reinforcement Learning), a multi-agent deep reinforcement learning framework that follows a centralized training and decentralized execution paradigm. The proposed method captures spatio-temporal dependencies through a novel dual-stage hypergraph attention mechanism that models interactions across both spatial and temporal hyperedges. In addition, a hybrid discrete action space is introduced to jointly determine the next signal phase configuration and its corresponding green duration, enabling more adaptive signal timing decisions. Experiments conducted on a corridor network under five traffic scenarios demonstrate that STDSH-MARL achieves strong overall multimodal performance, with substantial and relatively consistent reductions in tram waiting time, while improvements in bus waiting time are more variable across traffic scenarios. These results highlight the trade-off among overall network efficiency, tram priority, and bus service quality. Compared with state-of-the-art baseline methods, the proposed approach achieves superior overall performance. Further ablation studies confirm the contribution of each component of STDSH-MARL, with temporal hyperedges identified as the most influential factor driving the observed performance gains.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
