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 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.
Al-Ghamdi
Ali S
AS
College of Engineering, King Saud University, Riyadh, Saudi Arabia. asghamdi@ksu.edu.sa
D000059:Accidents / Q000401:mortality / Q000706:statistics & numerical data*; D000063:Accidents, Traffic / Q000517:prevention & control* / Q000706:statistics & numerical data; D000367:Age Factors; D001336:Automobiles; D006801:Humans; D016015:Logistic Models; D016017:Odds Ratio; D012307:Risk Factors; D012529:Saudi Arabia / Q000453:epidemiology