Risk analysis of road traffic accidents based on improved data mining method Online publication date: Mon, 16-Jan-2023
by Tianjun Feng; Tan Gao
International Journal of Simulation and Process Modelling (IJSPM), Vol. 18, No. 4, 2022
Abstract: According to the characteristics of road traffic accident data, two improved data mining methods are used to analyse the risk of accidents: nine accident-related factors are selected for discrete classification by weighted naive Bayes, the influence between factors is measured by weights and PMI thresholds, and the type of accident was predicted for a combination of factors. The accuracy of prediction increased from 83.98% to 87.02%. The traditional k-means algorithm is improved from three aspects: initial clustering centre, outlier point and distance measurement. Through these improvements, the computational complexity of clustering process is reduced and the clustering accuracy of accident-related factors is improved. On the one hand, the two methods can quantify the risk of accidents and facilitate the formulation of preventive measures; on the other hand, they can be used to improve the rationality of traffic safety evaluation.
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