Citation

Hasegawa H, Fujii M, Arimura M, Tamura T. J. East Asia Soc. Transp. Stud. 2007; 7: 2873-2880.

Copyright

(Copyright © 2007, Eastern Asia Society for Transportation Studies)

DOI

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PMID

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Abstract

In Japan, fatalities from traffic accidents are decreasing, but sacrifices of the traffic accidents are not negligible. So, traffic safety measures are still important. When considering the traffic safety measures, it is effective to extract dangerous locations with high fatality and injury accident rates and then analyze the details of the factors involved in such accidents. Due to numerous factors, however, it is difficult to effectively and efficiently process large quantities of traffic accident data. For this reason, previous traffic analyses are reviewed, and a Support Vector Machine (hereinafter referred to as "SVM"), which has become the focus of attention as a data mining method, is chosen. The SVM is applied to the traffic accident data analysis. The effectiveness of and problems surrounding a SVM are examined in this study. The classification rate of the SVM toward non-learning data was approximately 70%.