| Summary: | 碩士 === 國立中央大學 === 土木工程研究所 === 93 === Abstract
Traffic accidents happen in many ways. The severity of traffic accidents vary by different situations. Suitable traffic analyzing and categorizing methods depends on correct and complete traffic accidents files.
In this research, data mining is applied to the traffic safety field and expects to point out the hidden factors of traffic accidents. By analyzing the relationship among people, vehicle, weather, and the design of roads, we utilize the skills of clustering to find out the relative severity and the relationships between factors of traffic accident. Regarding Accident Injury Severity as hierarchical standard and predictive goal and use Discriminant Analysis of multivariable analysis in the hope of setting up a suitable Severity Categorizing Model for Taiwan.
In the past research, 114177 traffic accidents happen on every highway system in Taiwan in 2003. The result of the study finds, in aspect of human factors, the accident injury severity is determined by the drivers’ qualification, equipments, and ages; in aspect of environment factors, the accident injury severity is determined by the movements of the signal, the state of road surface, and lights; in aspect of road factors, the highest class of danger is influenced by the separating among the speed lanes; in aspect of vehicle finds the effect of vehicle usage decreases with increasing dangerous degree, but different kinds of vehicles increases with increasing dangerous degree.
In Accident Injury Severity Discriminating and Categorizing Model, 7 levels of dangerous show the greatest declaring rate of its efficiency, and then decrease progressively with levels increased. Because the sentencing rate is up to 79.6%, we can know how effective this way is and prove that the four-aspect Severity Categorizing Model can really weigh Accident Injury Severity.
In Severity Categorizing Model, the influence percentage of four-aspects from high to low is aspect of vehicle danger, aspect of road danger, aspect of environmental danger, and aspect of people danger. And the raise of each enhances the Accident Injury Severity at the same time, especially the aspect of vehicle and road.
Key Words：Accident Injury Severity、Data mining、Clustering、Discriminant Analysis、Severity Categorizing Model
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