Data Mining in Traffic Accident－A Case Study for Bus
Date Issued
2009
Date
2009
Author(s)
Wang, I-Ching
Abstract
According to the statistic data from National Police Agency, traffic accidents increase year over year. Averagely, 2,800 people die in traffic accidents. Moreover, in every thousand cars that cause deadly car accidents, bus is the main trouble maker. Thus, once a car accident happens, bus accidents usually lead to more deaths than other car accidents. Bus accidents usually include speeding, drunk driving, driving against traffic regulations, and brake failing…etc. Therefore, in this thesis, I try to do traffic accidents analysis by using data mining, and figure out the main causes of bus accidents. In addition, I use cluster analysis to find out the most homogeneous class which causes bus accidents. As a result, I use chi-square test to verify the validity of the cluster and apply it to discriminant analysis which can determine the discriminant function and predict the accuracy of classification, so that the variable which causes bus accidents will come out. In this research, the bus accidents data from 2003 to 2007 are included; the total number of the data is 15514, which can be found in National Police Agency. The result of this research includes the Training Samples and the Test Samples from 2003 to 2007, which’s accuracy of classification are all over ninety percent. Therefore, the identification is quite excellent. The major variables which cause bus accidents from 2003 to 2007 are accident causes, which months, what kind of road, speed limit, and curb systems. Among these factors, accident causes are the most. And results for this strategy to improve.
Subjects
Traffic Safety
Accident Analysis
Data Mining
Cluster Analysis
Discriminant Analysis
Factor Analysis
SDGs
Type
thesis
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