Brief Industry Paper: RTLight: Digital Twin-Based Real-Time Federated Traffic Signal Control

Although Reinforcement Learning (RL)-based methods have been widely researched in Traffic Signal Control (TSC), they still suffer from the problems of poor adaptation to real-world traffic scenarios and slow convergence to optimized solutions. This is because RL-based TSC methods have a high dependency on accurate modeling of the environment. With transportation infrastructure constraints, some vehicle dynamic information in the road network is difficult to obtain in real-time, which strongly limits the capability of RL agents. To address this problem, we propose a novel real-time federated traffic signal control system named RTLight, which can efficiently control traffic lights in real-time for multi-intersection scenarios. Based on the digital twin, the RL agent can obtain sufficient traffic information and interact with the environment in real time. Inspired by federated learning, our system supports knowledge sharing among intersections, which improves the overall convergence rate and control performance. Note that, we have deployed our RTLight system for large-scale application validation in Xishan district, Wuxi, China. Experimental results obtained from various real-world traffic scenarios demonstrate that RTLight can significantly improve the control performance.