Reliability-based assessment of road design features and crash risk using a socio-economic index for safety prioritization

This study investigates the relationship between road design features and crash risk on a 186 km segment of Highway No. 36 in Iran. A socio-economic risk index was developed by integrating Empirical Bayes crash predictions, severity-based social crash costs, and construction cost estimates. This index was incorporated into a reliability framework, where limit-state functions and Monte Carlo simulation were used to compute the exceedance probability of crash risk. Geometric data were collected through field surveys, and traffic and crash data were obtained from the Khorasan Razavi Road Maintenance and transportation organization’s database (2019–2023). The results show that horizontal curves have the highest crash risk, while segments longer than 4 km exhibit the lowest values. Crash risk also increases with wider lanes, gravel shoulders, greater shoulder widths, and embankment slopes steeper than 4%. Grades between 0 and 3% reduce risk, whereas steeper grades elevate it. Guardrails demonstrate mixed effects, reducing risk at lower levels but not consistently at higher ones. The reliability-based probabilistic framework integrates crash data, societal costs with severity, and construction costs to systematically prioritize safety interventions, offering a clear methodological advantage over deterministic approaches.