Behavioral Adaptation and Risk Compensation in Horizontal Curve Crashes: A Multidimensional Accident Cost Rate Framework

Horizontal curve crashes represent a major road safety challenge worldwide. This study presents a multidimensional accident cost rate (ACR) framework for identifying high-risk horizontal curves using infrastructure, pavement friction, environmental conditions, and behavioral adaptation indicators. We developed a data-driven pipeline analyzing 2,563 km of Austrian rural roads over 12 years (2012–2023), combining police-reported crashes with RoadSTAR geometry/friction data and traffic exposure. The Accident Cost Rate (ACR) metric was applied at section and network levels, with a robust Hotspot_50 rule (ACR _section > 1.5 × ACR _road ) for prioritization. We employed bootstrap intervals, effect sizes, Spearman's correlation, and k-means clustering to derive actionable risk profiles. Among 3,333 curve crashes, we identified a behavioral adaptation paradox: highest accident costs occurred under dry pavement and daylight conditions. Curvature showed strong positive association with ACR (ρ = 0.634, p < 0.001), while AADT was negatively correlated (ρ = -0.508, p < 0.001). Counterintuitively, high friction (µ > 0.60) correlated with increased ACR, suggesting risk compensation. Cluster analysis revealed four distinct risk profiles enabling targeted interventions. The framework provides transportation agencies with a transferable, computationally efficient tool for cost-weighted safety prioritization. Results demonstrate that behavioral adaptation can offset infrastructural safety gains, necessitating behavior-aware countermeasures in Vision Zero strategies.