Exploring driver injury severity patterns and causes in low visibility related single-vehicle crashes using a finite mixture random parameters model

Low visibility is consistently considered as a hazardous factor due to its potential leading to severe fatal crashes. However, unlike the other inclement weather conditions that have attracted extensive research interests, only a few studies have been conducted to investigate the impacts of risk factors on driver injury severity outcomes in low visibility related crashes. A three-year crash dataset including all low visibility related crashes from 2010 to 2012 in four South Central states, i.e., Arkansas, Louisiana, Texas, and Oklahoma, is adopted in this study. A finite mixture random parameters approach is developed to interpret both within-class and between-class unobserved heterogeneity among crash data. After a careful comparison, a two-class finite mixture random parameter model with normal distribution assumptions is selected as the final model. Estimation results show that three variables, including young (specific to injury, I), male (specific to serious injury and fatality, F), and large truck (specific to serious injury and fatality, F), are found to be normally distributed and have significant impacts on driver injury severities. Variables with fixed effects including rural, wet, 60 mph or higher, no statutory limit, dark, Sunday, curve, rollover, light truck, old, and drug/alcohol impaired also have significant influences on driver injury severities. This study provides an insightful understanding of the impacts of these variables on driver injury severity outcomes in low visibility related crashes, and a beneficial reference for developing countermeasures and strategies to mitigate driver injury severities under these conditions.