Traffic signal control (TSC) plays a crucial role in the intelligent transportation system. Among existing TSC approaches, Proactive Traffic Signal Control (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.

The application of reinforcement learning in traffic signal control (TSC) has been extensively researched and yielded notable achievements. However, most existing works for TSC assume that traffic data from all surrounding intersections is fully and continuously available through sensors. In real-world applications, this assumption often fails due to sensor malfunctions or data loss, making TSC with missing data a critical challenge. To meet the needs of practical applications, we introduce DiffLight, a novel conditional diffusion model for TSC under data-missing scenarios in the offline setting. Specifically, we integrate two essential sub-tasks, i.e., traffic data imputation and decision-making, by leveraging a Partial Rewards Conditioned Diffusion (PRCD) model to prevent missing rewards from interfering with the learning process. Meanwhile, to effectively capture the spatial-temporal dependencies among intersections, we design a Spatial-Temporal transFormer (STFormer) architecture. In addition, we propose a Diffusion Communication Mechanism (DCM) to promote better communication and control performance under data-missing scenarios. Extensive experiments on five datasets with various data-missing scenarios demonstrate that DiffLight is an effective controller to address TSC with missing data. The code of DiffLight is released at https://github.com/lokol5579/DiffLight-release.