UniTSA: A Universal Reinforcement Learning Framework for V2X Traffic Signal Control

Maonan Wang, Xi Xiong, Yuheng Kan, Chengcheng Xu, Man-On Pun

Abstract: Traffic congestion is a persistent problem in urban areas, which calls for the development of effective traffic signal control (TSC) systems. While existing Reinforcement Learning (RL)-based methods have shown promising performance in optimizing TSC, it is challenging to generalize these methods across intersections of different structures. In this paper, we introduce UniTSA, a universal RL framework tailored for Vehicle-to-Everything (V2X) environments, aimed at overcoming these generalization hurdles. Our framework is equipped with a novel agent architecture that utilizes a junction matrix to uniformly represent intersection states, making it applicable to a variety of intersection designs. Additionally, UniTSA incorporates traffic state augmentation techniques specifically developed for TSC systems. These techniques leverage the rotational symmetry inherent to intersections and emphasize the relative positioning of vehicles over simple vehicle counts, enhancing the model's adaptability to changing traffic conditions and unfamiliar scenarios. We also integrate the Low-Rank Adaptation (LoRA) method, allowing for efficient model customization to specific intersections with minimal additional training. Extensive evaluations conducted on the Simulation of Urban MObility (SUMO) platform, featuring a range of intersection layouts, confirm UniTSA's robust performance. Our results indicate a significant advancement in the development of scalable TSC solutions suitable for diverse V2X applications.