Wrong-way driving crashes: A random-parameters ordered probit analysis of injury severity
| Autor: | Ramin Shabanpour, Mahdi Pour-Rouholamin, Huaguo Zhou, Mohammad Jalayer, Nima Golshani |
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| Rok vydání: | 2018 |
| Předmět: |
Adult
Male Automobile Driving Computer science Human Factors and Ergonomics Ordered probit Crash Wrong-way driving Surface conditions Young Adult Injury Severity Score 0502 economics and business Statistics Humans 0501 psychology and cognitive sciences Crash data Safety Risk Reliability and Quality 050107 human factors Aged Road user 050210 logistics & transportation 05 social sciences Accidents Traffic Public Health Environmental and Occupational Health Seat Belts Middle Aged Random parameters United States Logistic Models Alabama Wounds and Injuries Female Illinois Automobiles human activities |
| Zdroj: | Accident Analysis & Prevention. 117:128-135 |
| ISSN: | 0001-4575 |
| DOI: | 10.1016/j.aap.2018.04.019 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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