Proactive-XLight: Proactive Traffic Signal Control With Pluggable and Reliable Traffic Prediction

Yang Jiang, Shengnan Guo, Hanyang Chen, Xiaowei Mao, Youfang Lin, Huaiyu Wan

Abstract: Traffic signal control (TSC) plays a crucial role in the intelligent transportation system. Among existing TSC approaches, Proactive TSC (PTSC) predicts future traffic states at intersections and proactively adjusts control policies. It is evident that PTSC methods are highly effective in alleviating both current and future traffic congestion at intersections. However, existing PTSC methods focus on point estimation prediction while neglecting prediction reliability. Additionally, they fail to adaptively coordinate between current and future traffic states for optimal control. To address these limitations, we propose an innovative Proactive-Plugin that can be combined with existing TSC methods to enhance the accuracy and robustness of traffic signal control policies. This plugin enhances two critical aspects: 1) Prediction reliability is achieved through Fine-grained Traffic Uncertainty Quantification. This module generates probabilistic forecasts along with confidence intervals to explicitly indicate the credibility of the predictions. 2) Coordination adaptiveness is enabled by a Current-Future Tradeoff Integration mechanism. This mechanism dynamically adjusts the relative influence of current traffic states and probabilistic forecasts on control policies. To further ensure robustness, we design a multi-task joint optimization to reduce the negative impact of inaccurate predictions during training. Experimental results on six real-world datasets demonstrate consistent improvements in traffic efficiency, validating the effectiveness of our approach.

External IDs:dblp:journals/tmc/JiangGCMLW25