title: Pattern recognition for road traffic accident severity in Korea.
abstract: 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.
authors.affiliation: Department of Industrial Systems Engineering, Yonsei University, Seoul, Korea. sohns@yonsei.ac.kr
authors.forename: S Y
authors.initials: SY
authors.lastname: Sohn
authors.forename: H
authors.initials: H
authors.lastname: Shin
mesh_terms: D000063:Accidents, Traffic / Q000706:statistics & numerical data; D003663:Decision Trees; D005163:Factor Analysis, Statistical; D006801:Humans; D007723:Korea; D016015:Logistic Models; D016571:Neural Networks, Computer