Wrong-way driving crashes: A random-parameters ordered probit analysis of injury severity. (August 2018)

• Record Type: Journal Article
• Authors: Jalayer, Mohammad; Shabanpour, Ramin; Pour-Rouholamin, Mahdi; Golshani, Nima; Zhou, Huaguo
• Is Part Of: Accident analysis and prevention. Volume 117(2018)
• Page Start: 128
• Page End: 135
• Publication Date: 2018-08
• Subjects: Wrong-way driving -- Random-parameters ordered probit model -- Safety countermeasures; Accidents -- Prevention -- Periodicals; Accident Prevention -- Periodicals; Accidents -- Prévention -- Périodiques; 363.106
• DOI: 10.1016/j.aap.2018.04.019
• Languages: English
• ISSNs: 0001-4575

Abstract

Highlights: A random-parameters ordered probit model was developed to analyze injury severity of wrong-way driving crashes. This approach takes into account the unobserved effects of roadway, vehicle, driver, etc. Driver age, driver condition, lighting conditions, etc. significantly contribute to the injury severity of crashes. Winter, urban setting, and number of vehicles involving in crash were found to be random parameters. Abstract: In the context of traffic safety, whenever a motorized road user moves against the proper flow of vehicle movement on physically divided highways or access ramps, this is referred to as wrong-way driving (WWD). WWD is notorious for its severity rather than frequency. Based on data from the U.S. National Highway Traffic Safety Administration, an average of 355 deaths occur in the U.S. each year due to WWD. This total translates to 1.34 fatalities per fatal WWD crashes, whereas the same rate for other crash types is 1.10. Given these sobering statistics, WWD crashes, and specifically their severity, must be meticulously analyzed using the appropriate tools to develop sound and effective countermeasures. The objectives of this study were to use a random-parameters ordered probit model to determine the features that best describe WWD crashes and to evaluate the severity of injuries in WWD crashes. This approach takes into account unobserved effects that may be associated with roadway, environmental, vehicle, crash, and driver characteristics. To that end and given the rareness of WWD events, 15 years of crash data from the states of Alabama and Illinois were obtained and compiled. Based on this data, a series of contributing factors including responsible driver characteristics, temporal variables, vehicle characteristics, and crash variables are determined, and their impacts on the severity of injuries are explored. An elasticity analysis was also performed to accurately quantify the effect of significant variables on injury severity outcomes. According to the obtained results, factors such as driver age, driver condition, roadway surface conditions, and lighting conditions significantly contribute to the injury severity of WWD crashes.