Citation

Al-Bdairi NS, Hernandez S. Accid. Anal. Prev. 2017; 102: 93-100.

Affiliation

School of Civil and Construction Engineering, Oregon State University, 101 Kearney Hall, Corvallis, OR 97331-3212, United States. Electronic address: sal.hernandez@oregonstate.edu.

Copyright

(Copyright © 2017, Elsevier Publishing)

DOI

10.1016/j.aap.2017.02.024

PMID

28268204

Abstract

In recent years, there has been an increasing interest in understanding the contributory factors to run-off-road (ROR) crashes in the US, especially those where large trucks are involved. Although there have been several efforts to understand large-truck crashes, the relationship between crash factors, crash severity, and ROR crashes is not clearly understood. The intent of this research is to develop statistical models that provide additional insight into the effects that various contributory factors related to the person (driver), vehicle, crash, roadway, and environment have on ROR injury severity. An ordered random parameter probit was estimated to predict the likelihood of three injury severity categories using Oregon crash data: severe, minor, and no injury. The modeling approach accounts for unobserved heterogeneity (i.e., unobserved factors). The results showed that five parameter estimates were found to be random and normally distributed, and varied across ROR crash observations. These were factors related to crashes that occurred between January and April, raised median type, loss of control of a vehicle, the indicator variable of speed not involved, and the indicator variable of two vehicles or more involved in the crashes. In contrast, eight variables were found to be fixed across ROR observations. Looking more closely at the fixed parameter results, large-truck drivers who are not licensed in Oregon have a higher probability of experiencing no injury ROR crash outcomes, and human related factor, fatigue, increases the probability of minor injury. The modeling framework presented in this work offers a flexible methodology to analyze ROR crashes involving large trucks while accounting for unobserved heterogeneity. This information can aid safety planners and the trucking industry in identifying appropriate countermeasures to help mitigate the number and severity of large-truck ROR crashes.

Copyright © 2017. Published by Elsevier Ltd.

Language: en

Keywords

Injury severity; Large-truck safety; Ordered random parameter probit model; Run-off-road crashes; Unobserved heterogeneity