Multi-graph spatio-temporal network for traffic accident risk forecasting

Abstract

Accurately predicting traffic accident risk is crucial for preventing traffic accidents and enhancing road traffic safety. Extensive approaches have been proposed for this task. However, existing methods face three main challenges: (i) difficulty in capturing complex spatial correlations, especially semantic dependencies; (ii) neglect of differences between daily and weekly temporal patterns; and (iii) equal treatment of heterogeneous semantic and geographical spatio-temporal features, which hampers predictive performance. To address these limitations, a multi-graph spatio-temporal network for predicting traffic accident risk is proposed, referred to as MG-STNET. Specifically, the Multi-Graph Spatial Network (MGSNet) and the Geographical Spatial Network (GeoSNet) are first introduced to capture semantic spatial dependencies across all regions and geographical correlations among adjacent regions, respectively. Temporal Blocks is then employed to model daily and weekly accident patterns separately. Finally, an adaptive channel fusion gate is integrated to automatically balance heterogeneous semantic and geographical spatio-temporal features. Experiments conducted on the NYC and Chicago datasets evaluate model performance under two aspects: all-day and high-frequency traffic accident periods. The results demonstrate that MG-STNET consistently outperforms baseline methods and highlight the importance of each model component.