DiffLight: A Partial Rewards Conditioned Diffusion Model for Traffic Signal Control with Missing Data

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

Keywords: Traffic signal control, Reinforcement learning, Diffusion model, Spatial-temporal data

TL;DR: We propose DiffLight, a conditional diffusion framework, unifying traffic data imputation and decision-making for TSC with missing data.

Abstract: 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.

Primary Area: Machine learning for other sciences and fields

Submission Number: 18530