Probabilistic Graph Neural Networks for Traffic Signal Control
Ting Zhong, Zheyang Xu, Fan Zhou
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.