Using logistic regression to estimate the influence of accident factors on accident severity
Using logistic regression to estimate the influence of accident factors on accident severity
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DOI：
10.1016/s0001-4575(01)00073-2
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发表时间：
2002-11-01
影响因子：
5.9
通讯作者：
Al-Ghamdi, AS
中科院分区：
文献类型：
--
作者：
Al-Ghamdi, AS
Logistic回归被应用到交通警察记录中收集的事故相关数据，以检查几个变量对事故严重程度的贡献。共抽取了560名涉及严重事故的受试者。本研究中的事故严重程度（因变量）是一个二分变量，分为致命性和非致命性两类。因此，每一个被抽样的受试者被分类为致命或非致命事故。由于该因变量的二进制性质，发现逻辑回归方法是合适的。从警方的事故报告中获得的九个自变量中，两个被发现最显着相关的事故严重程度，即事故的位置和原因。统计解释给出的模型开发的估计的优势比的概念。研究结果表明，在这项研究中使用的逻辑回归是一个很有前途的工具，在提供有意义的解释，可用于未来的安全改进利雅得。(C)2002爱思唯尔科技有限公司版权所有。
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. (C) 2002 Elsevier Science Ltd. All rights reserved.