Name
Title | ||
|---|---|---|
Network-wide traffic signal control based on the discovery of critical nodes and deep reinforcement learning |
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1080/15472450.2018.1527694 | Journal of Intelligent Transportation Systems |
Keywords | Field | DocType |
Critical node,deep reinforcement learning,traffic signal controller,tripartite graph | Road networks,Traffic signal,Optimal control,Simulation,Computer network,Traffic efficiency,Engineering,Reinforcement learning | Journal |
Volume | Issue | ISSN |
24 | 1 | 1547-2450 |
Citations | PageRank | References |
2 | 0.39 | 15 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ming Xu | 1 | 10 | 2.62 |
| Jianping Wu | 2 | 408 | 53.61 |
| Ling Huang | 3 | 3 | 1.09 |
| Rui Zhou | 4 | 21 | 17.94 |
| Tian Wang | 5 | 2 | 0.39 |
| Dongmei Hu | 6 | 3 | 0.75 |