Hit-and-Run Crashes: Use of Rough Set Analysis with Logistic Regression to Capture Critical Attributes and Determinants
Abstract
In this paper, an innovative mathematical tool, rough set analysis (RSA), combined with logistic regression modeling, is used to understand the key factors associated with hit-and-run collisions in Hawaii. After a description of the nature of the problem in Hawaii and some background on the RSA, the methods are applied to a comprehensive database of police-reported accidents over the period 2002 to 2005. RSA is used to extract the key determinants of hit-and-run collisions. With the information from the RSA, a logistic regression model is built to explain the key factors associated with hit-and-run crashes in Hawaii. Factors such as being (a) a male, (b) a tourist, and (c) intoxicated and driving a stolen vehicle are strong predictors of hit-and-run crashes. In addition to the obvious human factors associated with these crashes, there are interesting roadway features, such as horizontal alignment, weather, and lighting, that are also significantly related to hit-and-run crashes. Some suggestions for reducing hit-and-run crashes as well as opportunities for additional research are identified.
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Article first published online: January 1, 2008
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