Run-off-road (ROR) crashes in the United
States have become a major cause of serious injuries and fatalities. A
significant portion of run-off-road crashes are single vehicle crashes
that occur due to collisions with fixed objects and overturning. These crashes
typically tend to be more severe than other types of crashes. And the economic costs associated with this type of crashes are
significantly high. Therefore, it is necessary to identify the factors that are
associated with single vehicle ROR crashes so that effective remedies can be
developed to reduce the severity of ROR crashes.
In this study, single vehicle
run-off-road crashes that occurred between 2004 and 2008 were extracted from
Kansas Accident Reporting System (KARS) database to identify the important
factors that affected their severity. Different driver, vehicle, road, crash,
and environment related factors that influenced crash severity were identified by using
binary logit models. Three models were developed to take different
levels of crash severity as the response variables.
The first model
taking fatal or incapacitating crashes as the response variable seemed to
better fit the data than the other two developed models. The variables that
were found to increase the probability of run-off-road crash severity were
driver related factors such as driver ejection, being an older driver, alcohol
involvement, license state, driver being at fault, medical condition of
the driver; road related factors such as speed, asphalt road surface, dry
road condition; time related factors such as crashes occurring
between 6 pm and midnight; environment related factors such as daylight;
vehicle related factors such as being an SUV, motorcycles, vehicle getting
destroyed or disabled, vehicle maneuver being straight or passing; and fixed
object types such as trees and ditches.
In conclusion, the use of logistic regression model in predicting the factors and
affecting crash severity is a useful tool and could be considered to provide
more accurate estimations than other methods. The variables that are identified
in this study as influential towards crash severity can help in developing
appropriate countermeasures to reduce the severity of single vehicle ROR
crashes.
Article by Sunanda
Dissanayake and Uttara Roy, from Kansas State University, USA.
Full access: http://mrw.so/Q8y2h
Image by Reuben Richardson, from Flickr-cc.
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