Guang-He Li

Attention-based Spatio-Temporal Graph Convolutional Networks-enhanced deep reinforcement learning for adaptive traffic signal control in urban traffic networks.

A novel adaptive traffic signal control framework by integrating Attention-based Spatio-Temporal Graph Convolutional Networks (ASTGCN) with Multi-Agent Deep Deterministic Policy Gradient (MADDPG) is proposed, providing a scalable and data-driven solution for intelligent traffic signal control in urban traffic networks, supporting the development of smart mobility systems.