Path-Based Risk Segmentation of Road Networks with Exposure Modeling

Crash hotspot analysis has been widely studied in road traffic safety. Conventional approaches primarily rely on the spatial density or frequency of crash locations but fail to capture vehicle traversal patterns and segment-level exposure. In addition, when detailed traffic volume data are unavailable, it becomes difficult to assess risk while accounting for road exposure. In particular, Network Kernel Density Estimation (NKDE) is sensitive to bandwidth selection and remains limited in representing exposure-normalized, path-consistent risk at the road-segment level. To overcome these limitations, this study proposes a path-based risk segmentation framework that integrates crash paths with simulation-based exposure. Origin–crash coordinate pairs are extracted from crash reports, and vehicle paths are reconstructed over a road network. Monte Carlo simulation is used to estimate a relative exposure proxy across road segments and combine it with path-derived traversal patterns to compute segment-level risk. A case study in Daejeon Metropolitan City demonstrates that the proposed method addresses key limitations of NKDE by yielding more coherent risk segments and improving path alignment, and it identifies high-risk segments more effectively than the conventional NKDE baseline, particularly under small top-α% selection ratios, as measured by the path-based hit rate. This study provides a new perspective on crash risk analysis by shifting from point-based to path-based interpretation and by explicitly normalizing risk with an exposure proxy under data-limited conditions. It offers a practical framework for identifying high-risk segments at the road network level.