Shandong Science ›› 2023, Vol. 36 ›› Issue (5): 67-74.doi: 10.3976/j.issn.1002-4026.2023.05.009

• Traffic and Transportation • Previous Articles     Next Articles

Association analysis method for railway accident hazards based on the improved Apriori algorithm

WANG Ning(), CHANG Ximing(), YANG Xin, WU Jianjun   

  1. State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing 100044
  • Received:2022-12-11 Published:2023-10-20 Online:2023-10-12
  • Contact: CHANG Ximing E-mail:20120910@bjtu.edu.cn;xmchang@bjtu.edu.cn

Abstract:

The causes of railway accidents are difficult to determine as several hazards can lead to accidents. To prevent the occurrence of railway accidents, the hazards responsible for railway accidents should be analyzed, and the occurrence rules of previous railway accidents should be revealed. In this study,data mining analysis on railway accidents and hazards was conducted using the improved Apriori algorithm.Considering the severity of accident casualties, a new calculation method for support and confidence indicators was proposed to weigh and quantify railway accident factors.Furthermore, time constraints were added to explore association rules of hazards with corresponding railway accidents at different times. Using the actual UK railway accident data, the association rules between railway accidents and hazards were discovered, and effective preventive measures were formulated for actual cases. Results show that the improved Apriori algorithm can explore more association rules between railway accidents and hazards, which can play an important role in preventing railway accidents.

Key words: hazards, railway accidents, association rule, Apriori algorithm, data mining analysis

CLC Number: 

  • U298.5

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