PV-TSC: Learning to Control Traffic Signals for Pedestrian and Vehicle Traffic in 6G Era
Kangjie Xu, Junqin Huang, Linghe Kong, Jiadi Yu, Guihai Chen
Abstract
Recent advances in traffic signal control have witnessed the success of reinforcement learning. However, most of these approaches have focused on vehicle traffic and lack consideration for pedestrians. This can be attributed in part to the fact that the existing underlying technologies are not yet practical to deploy in real-world environments. Vision technologies, for example, can easily be obscured from view in reality. The direction of movement and position of pedestrians is difficult to estimate accurately. The emergence of 6G localization and tracking services offer new opportunities. With this base service, we intend to improve the efficiency, safety, and scalability of multi-intersection traffic signal control with mixed traffic flows. This problem is challenging for its coordination, scalability, and access of new traffic. To solve these challenges, we propose PV-TSC, a distributed reinforcement learning motivated traffic signal control with pedestrian access. We analyze different behaviors of pedestrian traffic, and integrate pedestrian traffic with the proven traffic signal control scheme for vehicle traffic. Finally, we conduct simulation experiments to illustrate the superiority of PV-TSC against classic methods, and further analyze the effectiveness of PV-TSC design by exploring its variants.