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This paper explores gender differences in injury severity risk using acomprehensive crash dataset including the driver, vehicle, environment, androadway characteristics. For the purpose of this study, only single vehiclecrashes that occurred in the Commonwealth of Virginia, collected in a five yearperiod from 2011 to 2015, were used. This study contributes to the literatureof crash analysis by incorporating an extensive dataset with normalizedattributes. The dataset used for model estimation integrated from two differentdata sources. These data sources include Virginia Traffic Records ElectronicData System (TREDS) to incorporate crash information and Virginia Base MappingProgram to incorporate roadway characteristics. A two level nested logit modelis developed for each gender, in order to relax the Independence of IrrelevantAlternatives (IIA) assumption. Several crash determinants including but notlimited to, drivers characteristics, drivers behavior, vehicle condition,weather conditions, roadway geometry and surface conditions were found to havean impact on both male and female drivers. This study is an assessment ofinfluence of different factors on crash severity segmented by gender. A fewinteresting results include; (i) female fatality risk exceeds male fatalityrisk when driving under the influence of alcohol, (ii) speeding contributes tolower fatality risk among female drivers compared to their male counterparts,(iii) poor vehicle conditions have no impact on injury risk to female driverswhile having an increased injury risk among male drivers, and (iv) whiledriving in a work zone area increases the risk of property damage crashes, theimpact is higher for male drivers compared to female drivers.

Gender Differences in Injury Severity Risk of Single-Vehicle Crashes in Virginia: A Nested Logit Analysis of Heterogeneity