Examining the crash severity on divided rural multilane highway segments using multilevel ordinal logistic models

Abstract

There are different types of severe crashes that occur on divided rural multilane highway segments. However, few research studies focused on the severity analysis of crashes on rural multilane segments, especially divided segments. Moreover, very few studies considered within-segment and within-crash correlations in the past, which would result in biased estimates in coefficients of contributing factors of the crashes occurring on divided rural multilane highway segments. Therefore, it is meaningful to figure out the risk factors for the severity of this kind of traffic crashes using multilevel ordinal logistic models to identify the effect of contributing factors more accurately. Crash data in California were employed to calibrate the model. Model fit assessment and comparison were employed to ensure the suitability of introducing the random effects. The data set was divided into three levels: segment level, crash level, and occupant level. Five major types of crashes, including head-on, sideswipe, angle, rear-end, and single-vehicle crashes, that occurred on divided rural multilane highway segments were investigated separately to figure out the contributing factors related to road geometrics, vehicle, and occupant variables. The results show that multilevel ordinal logistic modeling provides significantly better results compared to the model of traditional ordinal logistic modeling without considering the random effects. The contributing factors of different types of crashes are different. For example, the head-on crashes occurring on rural segments with street light are associated with a lower likelihood of severe injury of crashes. The sideswipe crashes on rural segments without curbs or with unpaved median types are associated with a higher likelihood of severe injury. The rear-end crashes on rural segments with higher design speed are associated with a higher likelihood of severe injuries. It is also shown from the modeling results that the use of cell phone while driving would increase the likelihood of severe injury of sideswipe, angle, and single-vehicle crashes.