Name
Abstract | ||
|---|---|---|
Intelligent traffic signal control is crucial for efficient transportation systems. Recent studies use reinforcement learning (RL) to coordinate traffic signals and improve traffic signal cooperation. However, they either design the state of agents in a heuristic manner or model traffic dynamics in a deterministic way. This work presents a variational graph learning model TSC-GNN (Traffic Signal Control via probabilistic Graph Neural Networks) to learn the latent representations of agents and generate Q-value while taking traffic uncertainty conditions into account. Besides, we explain the rationality behind our state design using transportation theory. Experimental results conducted on real-world datasets demonstrate our model's superiority, e.g., it achieves more than 8% traffic efficiency improvement compared with the state-of-the-art baselines. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/ICASSP39728.2021.9414829 | 2021 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP 2021) |
Keywords | DocType | Citations |
Traffic signal control, deep reinforcement learning, graph neural network, variational auto-encoders, multi-agent system | Conference | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ting Zhong | 1 | 15 | 4.83 |
| Zheyang Xu | 2 | 0 | 0.34 |
| Fan Zhou | 3 | 39 | 14.05 |