Examining driver injury severity in left-turn crashes using hierarchical ordered probit models

Few existing studies in the literature devoted efforts to examine the driver injury severity in left-turn crashes. To fill this research gap, this paper aims to provide a comprehensive study of the contributing factors of left-turn crashes and the corresponding injury severities.

The hierarchical ordered probit (HOPIT) model is first applied to study driver injury severity in left-turn crashes. The HOPIT model can overcome the limitations of traditional ordered probit models since its thresholds are always positive and ordered. It is a function of unique explanatory parameters that do not necessarily affect the ordered probability outcomes directly. Considering the driving condition during the wintertime could be significantly different from other seasons, this study divided the overall crash dataset into "winter" and "other-season" subsets based on the temperature, snowing condition, and other factors.

With the "other-season" dataset, results demonstrated that contributing factors, such as young drivers, male drivers, clear, light, and ramp intersection with crossroad, are associated with a decrease in injury severity. On the contrary, factors like drug, alcohol, disregard traffic control device, high-speed limit, the protected left-turn signal, etc., are related to an increase in injury severity. In winter, results revealed that only nine contributing factors are significant to the left-turn crash. Alcohol, disregard traffic control device, nighttime, high-speed limit, head-on collision, and state road are associated with an increase in injury severity. Also, two-vehicle involved, snow, ramp intersection with crossroad are related to a decrease in injury severity.

The HOPIT model is applied to examine contributing factors of left-turn crashes and the corresponding injury severity, based on left-turn crash records from 2010 to 2017 in Utah. Eighteen significant factors of left-turn crash injury severity are identified in the overall dataset. In seasons rather than winter, the significant factors are almost the same as that of the entire year. In the winter, less significant factors and different patterns are found compared with the overall crashes.