Network analysis on causes for serious traffic accidents based on text mining

doi:10.16265/j.cnki.issn1003-3033.2021.09.021

Abstract

Abstract: In order to study characteristics of serious traffic accidents so as to effectively prevent them and reduce injuries, 254 investigation reports of such accidents were processed by text mining technology. Then,32 high-weight keywords were extracted by using improved term frequency-inverse document frequency (TF-IDF) algorithm. Finally, a hierarchical model of traffic accident mechanism, based on network centrality analysis, core edge structure analysis and agglomerative subgroup analysis on accident causes, was constructed considering theory of human vehicle road system safety. The results show that the contribution values of serious road traffic accidents network rank as illegal behavior, potential safety hazards and improper operation from top to bottom, and in terms of illegal behavior, it goes in the order of overload, speeding, wrong lane use, and fatigue, etc. The coupling of direct causes for illegal behavior and improper operation with indirect ones for potential safety hazards constitute the fundamental reason for failure of safe operation system in major accidents.

Key words: text mining, serious traffic accidents, social network analysis, accident mechanism, hierarchical mode

CLC Number:

X928

HAN Tianyuan, TIAN Shun, LYU Kaiguang, LI Xuan, ZHANG Jiatao, WEI Lang. Network analysis on causes for serious traffic accidents based on text mining[J]. China Safety Science Journal, 2021, 31(9): 150-156.

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