A spatial graph learning framework for multi‐scale road safety management based on road‐curve features and open‐source data

Abstract

Horizontal and vertical curves significantly affect crash risk due to their impact on driver behavior, vehicle dynamics, and sight distance. However, their combined effects and spatial interactions remain underexplored in large‐scale safety assessments. To address limitations in high‐resolution geometric data and insufficient spatial modeling, this study proposes a geometry‐oriented crash risk assessment framework based on graph neural networks. Leveraging open‐source geospatial data, this study extracts fine‐grained curve features and constructs a GraphSAGE model to capture spatial dependencies among road segments. A dual‐graph architecture is developed to jointly encode both segment‐level and network‐level information. In large‐scale empirical evaluations, the proposed model exhibits excellent predictive performance (F1 > 0.985) and strong spatial correlation with historical crash distributions (r > 0.7). The model effectively identifies high‐risk segments characterized by poor geometric continuity or abrupt structural transitions, providing decision support for alignment optimization. The model effectively identifies high‐risk segments characterized by poor geometric continuity or abrupt structural transitions, thereby supporting informed decisions for alignment improvements. This research enhances the understanding of the geometry–safety relationship and offers a scalable, open‐source tool to support local and regional traffic safety interventions.