Towards Multi-agent Reinforcement Learning based Traffic Signal Control through Spatio-temporal Hypergraphs
CoRR(2024)
摘要
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.
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