Using logistic regression to estimate the influence of accident factors on accident severity

Logistic regression was applied to accident-related data collected from traffic police records in order to examine the contribution of several variables to accident severity. A total of 560 subjects involved in serious accidents were sampled. Accident severity (the dependent variable) in this study...

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Bibliographic Details
Published in:Accident analysis and prevention 2002-11, Vol.34 (6), p.729-741
Main Author: Al-Ghamdi, Ali S
Format: Article
Language:English
Subjects:
Accident severity
Accidents - mortality
Accidents - statistics & numerical data
Accidents, Traffic - prevention & control
Accidents, Traffic - statistics & numerical data
Age Factors
Automobiles
Humans
Logistic Models
Logistic regression
Odds Ratio
Risk Factors
Saudi Arabia - epidemiology
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Summary:Logistic regression was applied to accident-related data collected from traffic police records in order to examine the contribution of several variables to accident severity. A total of 560 subjects involved in serious accidents were sampled. Accident severity (the dependent variable) in this study is a dichotomous variable with two categories, fatal and non-fatal. Therefore, each of the subjects sampled was classified as being in either a fatal or non-fatal accident. Because of the binary nature of this dependent variable, a logistic regression approach was found suitable. Of nine independent variables obtained from police accident reports, two were found most significantly associated with accident severity, namely, location and cause of accident. A statistical interpretation is given of the model-developed estimates in terms of the odds ratio concept. The findings show that logistic regression as used in this research is a promising tool in providing meaningful interpretations that can be used for future safety improvements in Riyadh.
ISSN:0001-4575
1879-2057
DOI:10.1016/S0001-4575(01)00073-2