Network-wide traffic signal control based on the discovery of critical nodes and deep reinforcement learning

作者：Xu, Ming; Wu, Jianping; Huang, Ling^*; Zhou, Rui; Wang, Tian; Hu, Dongmei

来源：Journal of Intelligent Transportation Systems, 2020, 24(1): 1-10.

DOI：10.1080/15472450.2018.1527694

• Critical node
• deep reinforcement learning
• traffic signal controller
• tripartite graph

To improve the traffic efficiency of city-wide road networks, we propose a traffic signal control framework that prioritizes the optimal control policies on critical nodes in road networks. In this framework, we first use a data-driven approach to discover the critical nodes. Critical nodes are identified as nodes that would cause a dramatic reduction in the traffic efficiency of the road network if they were to fail. This approach models the dynamic of road networks using a tripartite graph based on the vehicle trajectories and can accurately identify the city-wide critical nodes from a global perspective. Second, for the discovered critical nodes, we introduce a novel traffic signal control approach based on deep reinforcement learning; this approach can learn the optimal policy via constantly interacting with the road network in an iterative mode. We conduct several experiments with a transportation simulator; the results of experiments show that the proposed framework reduces the average delay and travel time compared to the baseline methods.

• 单位
  清华大学