Assessing the effectiveness of deer warning signs

Details

• Creators:
  Meyer, Eric
• Corporate Creators:
  K-TRAN (Research program)
• Subject/TRT Terms:
  • Crash data
  • Deer
  • Warning signs
  • Evaluation and assessment
  • Risk assessment
  • Transportation safety
  • Tables (Data)
  • Charts
  • Accidents
  • Deer
  More +
• Publication/ Report Number:
  K-TRAN: KU-03-6
• Resource Type:
  Research Paper
• Geographical Coverage:
  Kansas
• Edition:
  Final Report; April 2002 - January 2005
• Corporate Publisher:
  Kansas. Dept. of Transportation
• 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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• Format:
  PDF
• Funding:
  C1349
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/5621/dot_5621_DS1.pdf
• File Type:
  [PDF - 2.91 MB]
• Collection(s):
  US Transportation Collection
• Main Document Checksum:
  urn:sha256:f04c97dcfeebceffd700c64b60347d436ed9e2cf86a04d352f0f9de580ae4024