Prioritising Safety Treatment of Rural Corridors Using Curve Context

Horizontal curves are a significant contributor to crash risk on rural road networks. Historically, high-risk curves are identified reactively, based on examination of crash history. However, to reduce the influence of random variation in observed crashes, proactive approaches, which estimate underlying crash risk, are increasingly preferred. To efficiently target road safety investment, a scalable, proactive approach to estimating rural crash risk is required. This paper presents a proactive corridor prioritisation framework for rural roads in the United States of America (US), based on horizontal curve risk. Road centreline geometry and speed limit data were used to estimate typical operating speeds and “curve context” – the degree to which the estimated operating speed of a curve differs from its safe traversal speed. A Safety Performance Function was used to estimate underlying risk, based on curve context and other geometric and traffic variables. Estimation and validation of the model using Arkansas and California datasets indicated that poor curve context is a significant predictor of crash risk. A case study of the Arkansas network is presented. The model allows estimation of crash risk across the US without reliance on crash data. Corridors are prioritised according to predicted out-of-context curve crash risk per unit length. This approach highlights corridors where treatments to address curve context are expected to deliver the greatest crash reductions relative to treatment costs. A map-based interactive tool is used to display corridor priorities. Targeting safety improvements on prioritised corridors is expected to produce significant reductions in rural road crashes.