  This paper explores gender differences in injury severity risk using a
comprehensive crash dataset including the driver, vehicle, environment, and
roadway characteristics. For the purpose of this study, only single vehicle
crashes that occurred in the Commonwealth of Virginia, collected in a five year
period from 2011 to 2015, were used. This study contributes to the literature
of crash analysis by incorporating an extensive dataset with normalized
attributes. The dataset used for model estimation integrated from two different
data sources. These data sources include Virginia Traffic Records Electronic
Data System (TREDS) to incorporate crash information and Virginia Base Mapping
Program to incorporate roadway characteristics. A two level nested logit model
is developed for each gender, in order to relax the Independence of Irrelevant
Alternatives (IIA) assumption. Several crash determinants including but not
limited to, drivers characteristics, drivers behavior, vehicle condition,
weather conditions, roadway geometry and surface conditions were found to have
an impact on both male and female drivers. This study is an assessment of
influence of different factors on crash severity segmented by gender. A few
interesting results include; (i) female fatality risk exceeds male fatality
risk when driving under the influence of alcohol, (ii) speeding contributes to
lower fatality risk among female drivers compared to their male counterparts,
(iii) poor vehicle conditions have no impact on injury risk to female drivers
while having an increased injury risk among male drivers, and (iv) while
driving in a work zone area increases the risk of property damage crashes, the
impact is higher for male drivers compared to female drivers.
