Details:

Creators:
Meyer, Eric
Corporate Creators:
K-TRAN (Research program)
Subject/TRT Terms:
[+]
Accidents Charts Crash Data Deer Evaluation And Assessment Mitigation Motor Vehicle Risk Assessment Rural Safety Tables (Data) Transportation Safety Warning Signage And Wildlife Warning Signs
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.


Format:
PDF
Funding:
C1349
Collection(s):
US Transportation Collection
Main Document Checksum:
[+]
urn:sha256:f04c97dcfeebceffd700c64b60347d436ed9e2cf86a04d352f0f9de580ae4024
Download URL:
https://rosap.ntl.bts.gov/view/dot/5621/dot_5621_DS1.pdf
File Type:

Supporting Files

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