Sohn S Y, Shin H
Department of Industrial Systems Engineering, Yonsei University, Seoul, Korea.
Ergonomics. 2001 Jan 15;44(1):107-17. doi: 10.1080/00140130120928.
An increasing number of road traffic accidents (RTA) in Korea has emerged as being harmful both for the economy and for safety. An accurately estimated classification model for several severity types of RTA as a function of related factors provides crucial information for the prevention of potential accidents. Here, three data-mining techniques (neural network, logistic regression, decision tree) are used to select a set of influential factors and to build up classification models for accident severity. The three approaches are then compared in terms of classification accuracy. The finding is that accuracy does not differ significantly for each model and that the protective device is the most important factor in the accident severity variation.
韩国道路交通事故（RTA）数量不断增加，已对经济和安全都造成危害。一个根据相关因素准确估计几种严重程度类型的RTA分类模型，可为预防潜在事故提供关键信息。在此，使用三种数据挖掘技术（神经网络、逻辑回归、决策树）来选择一组影响因素，并建立事故严重程度的分类模型。然后从分类准确性方面对这三种方法进行比较。研究结果是，每个模型的准确性没有显著差异，并且防护装置是事故严重程度变化中最重要的因素。
