Contagion Process Guided Cross-scale Spatio-Temporal Graph Neural Network for Traffic Congestion Prediction

Abstract

Frequent traffic congestion has a detrimental effect on our travel experience and the overall quality of urban life. Accurate prediction of traffic congestion plays a pivotal role in alleviating the congestion problem. However, existing traffic prediction approaches primarily focus on extracting its local changing patterns, overlooking the importance of incorporating global dynamic patterns. This presents three challenges: 1) Complicated spatial and temporal information exists in local (microscopic) traffic patterns; 2) The propagation and dissipation patterns of global (macroscopic) traffic congestion exhibit complex dynamics across time and space; 3) Modeling the interactions between macro and micro changing patterns of congestion remains unknown. In this paper, we present a novel framework for traffic congestion prediction that integrates microscopic and macroscopic cross-scale spatiotemporal modeling. Our approach utilizes contagion dynamics to characterize congestion propagation and recovery at the network-wide scale. Additionally, we employ a spatio-temporal graph neural network to capture local traffic patterns. A key contribution is the introduction of a differentiable micro-macro transformation mechanism, enabling the aggregation of microscopic states into macroscopic ones in a differentiable manner during model training. Further, we utilize the knowledge derived from macro contagion dynamics to constrain the micro traffic patterns by employing the physics-informed neural network. Experiments on three real-world datasets of traffic congestion demonstrate that our prediction model consistently outperforms the state-of-the-art baselines.