United States Department of Transportation
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Edition:Final Report; April 2002 - January 2005
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Abstract:Deer-vehicle crashes are a concern across the country, especially in states like Kansas, where most of the highway
mileage is rural. In Kansas, the concern led to passage of state statute 32-966. One result of this legislation was the
initiation of this study to consider the possible causes of deer-vehicle crashes and the implications with respect to
effective mitigation. Of particular interest was the effectiveness of deer warning signs. A broader need lies in the
development of better means of prioritizing segments for mitigative treatments, such as warning signs or fencing.
In Kansas, the most common countermeasure is the deer warning sign, even though its effectiveness is suspect,
and accident records have traditionally been used to identify locations for installation. This study examined the
effectiveness of deer warning signs by a comparison of crash rates before and after sign installation. Deer-vehicle
crashes were then studied with respect to an array of potential predictor variables with the intent of developing a
predictive model for deer-vehicle crash rate that could be used to prioritize segments for mitigative action. Two
separate analysis techniques were employed: Principal Component Analysis (PCA) followed by Multiple Linear
Regression, and Logistic Regression. Principal Component Analysis (PCA) was used to reduce colinearities prior to
applying linear regression. A total of 45 predictor variable were considered, 20 of which required field data collection.
Data was collected for 123 segments spanning 15 counties in Kansas. One hundred one data points were used for
model calibration and 22 data points were used for model validation.
Neither analysis approach was able to generate a model with sufficient predictive capability to justify its use in
prioritizing segments, but the analysis results provided some helpful insight into the nature of deer-vehicle crashes.
The insufficiency of the database to yield a predictive model is in itself a valuable realization. Models developed with
lesser data collection efforts must be held suspect unless they are supported by a strong validation effort.
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