VPLight: A Reinforcement Learning Approach for Traffic Signal Control with Pedestrian Dynamics

Xinyu Zhang, Zuohan Wu, Chen Jason Zhang, Libin Zheng, Peng Cheng, Jian Yin, Cyrus Shahabi

Abstract: Traffic Signal Control plays a vital role in modern traffic management. However, most existing methods focus exclusively on vehicle flow, neglecting the critical role of pedestrians, leading to suboptimal performance in intersections with mixed vehicle-pedestrian traffic. Pedestrian behavior presents unique challenges due to its irregularity and flexibility, such as non-lane-based movements and uncertain crossing directions, which cannot be modeled by existing methods. To address this limitation, we propose VPLight, a comprehensive framework designed to manage both Vehicle and Pedestrian dynamics in traffic signal control. Specifically, we first design the Pedestrian Feature Extractor to capture the spatiotemporal dynamics of pedestrian movement, offering a robust representation of their irregular patterns. Subsequently, to coordinate traffic signal control at multiple intersections, we develop a novel communication approach called V-Comm to enable effective integration among intersections. Extensive experiments show that VPLight outperforms state-of-the-art baselines with significant margins (up to +44.04%). Our results demonstrate that VPLight can remarkably address the challenges of mixed vehicle-pedestrian traffic control and enhance the overall traffic flow efficiency across the road network.

External IDs:doi:10.1109/tkde.2025.3641213