Injury severity of single-vehicle weather-related crashes on two-lane highways

Abstract

Roadway safety is immensely affected during adverse weather conditions. Therefore, this study seeks to quantify such effects on the injury severity of involved drivers in single-vehicle crashes on two-way highways. This should be done by identifying risk factors and accommodating the heterogeneous impacts of these factors by accounting for the heterogeneity in means and variances of random parameters in the estimated model using seven years of police-reported crash data from 2010 to 2016 in Oregon. The study findings confirm the superior performance of the mixed logit model with heterogeneity in the means and variances in terms of statistical fit comparable to the traditional mixed logit model and mixed logit model with heterogeneity in the means only. The estimation results reveal that some factors such as female drivers, fatigued drivers, crashes in the early morning (between 4:00 am and 8:00 am), and crashes that occurred in darkness with no streetlights were found to increase serious injury outcomes. The empirical findings can offer evidence-based insights to help in decision-making and countermeasure selection that is aimed at improving safety under adverse weather conditions.