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Crash Severity Analysis of Single Vehicle Run-off-Road Crashes

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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