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.

提出了一种新颖的交通信号控制系统框架，通过与多个相邻的边缘计算服务器协作收集道路网络上的交通信息，采用多智能体软 actor-critic 强化学习算法结合超图学习，以实现交通信号控制的智能化，优化交通流量，并且能够捕捉多个交叉口之间的复杂时空相关性，实现更智能、更具响应性的城市交通管理解决方案。