Title: Risk analysis of road traffic accidents based on improved data mining method

Authors: Tianjun Feng; Tan Gao

Addresses: School of Transportation Science and Engineering, Jilin Jianzhu University, Changchun, 130118, China ' School of Transportation Science and Engineering, Jilin Jianzhu University, Changchun, 130118, China

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.

Keywords: road traffic accidents; data mining; analysis of the risk of accidents; weighted naive Bayes; improved k-means.

DOI: 10.1504/IJSPM.2022.128271

International Journal of Simulation and Process Modelling, 2022 Vol.18 No.4, pp.253 - 266

Received: 31 Oct 2021
Received in revised form: 02 Jun 2022
Accepted: 08 Jul 2022
Published online: 16 Jan 2023 *

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