Differentiating high-frequency and high-severity hotspots: A robust risk-evolution-volume (REV) framework

Crash hotspot identification based solely on crash counts or density may over-prioritize high-volume roads and provide limited insight into whether a hotspot is associated with crash frequency or crash severity. This study proposes a generalizable risk-evolution-volume (REV) framework for differentiating high-frequency and high-severity crash hotspots, with Jiaozhou City, China, used as a case study based on crash data from 2022 to 2024. The framework integrates severity-weighted network kernel density estimation (SW-NKDE), threshold screening based on the cumulative distribution fitted by the Hurdle Gamma model, spatiotemporal evolution analysis, and a two-dimensional relative risk assessment using the critical crash rate (CCR) and a severity-weighted critical crash rate (SWCCR). The results reveal a clear divergence between crash frequency and crash severity patterns under comparable traffic conditions. Urban signalized intersections are more likely to exhibit relatively high crash frequency, whereas suburban segments, suburban unsignalized intersections, and some urban residential-interface locations are more likely to exhibit relatively high crash severity. The results further indicate that locations with seemingly high crash density do not necessarily represent abnormal risk once traffic exposure is taken into account. The S217 provincial highway provides an illustrative example of this pattern. Overall, the proposed framework could be adopted by traffic authorities within annual road safety management programs as a network-level screening tool for identifying hotspots with priority governance value, distinguishing hotspot types, and informing targeted countermeasures and resource allocation.