A Study on Traffic Accident Analysis Using Support Vector Machines

In Japan, the number of traffic accident fatalities has tended to decrease each year since 1992, but the number of traffic accidents has continued to increase and the number of injuries resulting from traffic accidents has also remained high. Therefore, measures to prevent traffic accidents are still important despite the decline in fatalities. When considering 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%.