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Abstract
It is a vital importance to analyze road traffic accidents in order to improve traffic security management. Currently, most of the traffic information analysis is limited to general statistical analysis, which is hard to explore the rules hiding in its dataset and also difficult to find the spatial distribution characteristics. This paper aims to analyze the road traffic accidents dataset based on data mining method of K-Means clustering and visualize the result as a map. Firstly, data are extracted for clustering road segments based on similar characteristics that lies on the dataset, i.e. the number of accidents, the number vehicles involved, and the number accidents’ victims. Secondly, the result of clustering are presented as a map that aims to assist the police officer in identifying and evaluating some black spot areas (accident prone areas) in a monthly period, hence monitoring the safety of highways users can be anticipated earlier.
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