Traffic signal control systems (TSCSs) are integral to intelligent traffic management, fostering efficient vehicle flow. Traditional approaches often simplify road networks into standard graphs, which results in a failure to consider the dynamic nature of traffic data at neighboring intersections, thereby neglecting higher-order interconnections necessary for real-time control. To address this, we propose a novel TSCS framework to realize intelligent traffic control. This framework collaborates with multiple neighboring edge computing servers to collect traffic information across the road network. To elevate the efficiency of traffic signal control, we have crafted a multi-agent soft actor-critic (MA-SAC) reinforcement learning algorithm. Within this algorithm, individual agents are deployed at each intersection with a mandate to optimize traffic flow across the entire road network collectively. Furthermore, we introduce hypergraph learning into the critic network of MA-SAC to enable the spatio-temporal interactions from multiple intersections in the road network. This method fuses hypergraph and spatio-temporal graph structures to encode traffic data and capture the complex spatial and temporal correlations between multiple intersections. Our empirical evaluation, tested on varied datasets, demonstrates the superiority of our framework in minimizing average vehicle travel times and sustaining high-throughput performance. This work facilitates the development of more intelligent and reactive urban traffic management solutions.

中文翻译：

---

通过时空超图实现基于多智能体强化学习的交通信号控制

交通信号控制系统 (TSCS) 是智能交通管理不可或缺的一部分，可促进高效的车流。传统方法往往将道路网络简化为标准图，这导致无法考虑邻近路口交通数据的动态性质，从而忽略了实时控制所需的高阶互连。为了解决这个问题，我们提出了一种新颖的 TSCS 框架来实现智能交通控制。该框架与多个相邻边缘计算服务器协作，收集整个道路网络的交通信息。为了提高交通信号控制的效率，我们设计了一种多智能体软演员批评家（MA-SAC）强化学习算法。在该算法中，每个交叉口都部署了单独的代理，其任务是共同优化整个道路网络的交通流量。此外，我们将超图学习引入 MA-SAC 的批评网络中，以实现道路网络中多个交叉口的时空交互。该方法融合超图和时空图结构来编码交通数据并捕获多个交叉口之间复杂的空间和时间相关性。我们的实证评估在不同的数据集上进行了测试，证明了我们的框架在最大限度地减少平均车辆行驶时间和维持高吞吐量性能方面的优越性。这项工作有助于开发更加智能和反应灵敏的城市交通管理解决方案。