A Composite Geospatial Index for Road Safety Risk: Integrating Crash Data with Roadway Characteristics

Road traffic accidents pose significant threats to public safety and urban infrastructure. While effective safety management is a critical component of sustainable transportation planning and public health, traditional approaches often rely heavily on identifying historical crash hotspots. These reactive methods frequently fail to account for the intrinsic environmental risk factors or overlook segments where crash frequency is low, yet the potential for severe outcomes remains high. To address this limitation, this paper presents a proactive, multi-factor geospatial model for calculating a comprehensive Road Safety Risk index at the individual road-segment level. Utilizing the City of Manningham, Victoria, as a case study, the research employs a GIS framework to synthesize official road network and historical crash data. The model incorporates four distinct risk dimensions: (1) accident frequency normalized by segment length; (2) a weighted accident severity index prioritizing serious incidents; (3) a normalized Speed Zone Factor; and (4) a Road Class Factor accounting for the functional hierarchy of the road network. The resulting risk map provides a granular and nuanced visualization of risk distribution, clearly identifying high-risk arterial corridors and intersections. Crucially, the analysis highlights road segments that, despite lower crash locations, pose significant threats due to a confluence of high speeds, road function, and crash severity. This replicable model serves as a powerful evidence-based tool for transport authorities, enabling a paradigm shift from reactive mitigation to proactive safety management.