| Paper ID | MLSP-47.6 |
| Paper Title |
PROBABILISTIC GRAPH NEURAL NETWORKS FOR TRAFFIC SIGNAL CONTROL |
| Authors |
Ting Zhong, Zheyang Xu, Fan Zhou, University of Electronic Science and Technology of China, China |
| Session | MLSP-47: Applications of Machine Learning |
| Location | Gather.Town |
| Session Time: | Friday, 11 June, 14:00 - 14:45 |
| Presentation Time: | Friday, 11 June, 14:00 - 14:45 |
| Presentation |
Poster
|
| Topic |
Machine Learning for Signal Processing: [MLR-APPL] Applications of machine learning |
| IEEE Xplore Open Preview |
Click here to view in IEEE Xplore |
| Virtual Presentation |
Click here to watch in the Virtual Conference |
| 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 learn- ing 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. |
