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Fa H, Shuai B, Yang Z, Niu Y, Huang W. Mining the accident causes of railway dangerous goods transportation: A Logistics-DT-TFP based approach. Accid Anal Prev 2024; 195:107421. [PMID: 38061291 DOI: 10.1016/j.aap.2023.107421] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/19/2023] [Revised: 11/13/2023] [Accepted: 12/02/2023] [Indexed: 12/30/2023]
Abstract
Accurately and quickly mining the hidden information in railway dangerous goods transportation (RDGT) accident reports has great significance for its safety management. In this paper, a data mining method Logistics-DT-TFP is proposed for analysing the causes of RDGT accidents. Firstly, analyse the transportation process, extract the cause of the accident, and classify the severity of the accident. Then, using ordered multi-classification Logistic regression for correlation calculation, qualitatively judge and quantitatively analyse the relationship between each cause and the severity of the accident. The feature tags of the Decision Tree (DT) are screened, the C5.0 algorithm is used to obtain the accident coupling rules. Next, the FP-Growth algorithm is used to mine frequent itemsets, and TOP-K is used to improve it and output effective association rules with the degree of lift as the indicator, which avoids repeated traversal of the database, shortens the time complexity, and reduces the impact of the minimum support setting on the calculation results. The degree of lift among the causes in the coupling chain is calculated as a complement to the extraction of coupling rules. Finally, based on the analysis and mining results of case study, the management strategies for railway dangerous goods are proposed.
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Affiliation(s)
- Huiyan Fa
- School of Transportation and Logistics, Southwest Jiaotong University, Chengdu Sichuan, 611756 China; Institute of System Science and Engineering, Southwest Jiaotong University, Chengdu Sichuan, 611756 China
| | - Bin Shuai
- School of Transportation and Logistics, Southwest Jiaotong University, Chengdu Sichuan, 611756 China; Institute of System Science and Engineering, Southwest Jiaotong University, Chengdu Sichuan, 611756 China; National United Engineering Laboratory of Intergrated and Intelligent Transportation, Southwest Jiaotong University, Chengdu Sichuan, 611756 China; National Engineering Laboratory of Integrated Transportation Big Data Application Technology, Southwest Jiaotong University, Chengdu Sichuan, 611756 China
| | - Zhenlong Yang
- School of Transportation and Logistics, Southwest Jiaotong University, Chengdu Sichuan, 611756 China; Institute of System Science and Engineering, Southwest Jiaotong University, Chengdu Sichuan, 611756 China
| | - Yifan Niu
- School of Transportation and Logistics, Southwest Jiaotong University, Chengdu Sichuan, 611756 China; Institute of System Science and Engineering, Southwest Jiaotong University, Chengdu Sichuan, 611756 China
| | - Wencheng Huang
- School of Transportation and Logistics, Southwest Jiaotong University, Chengdu Sichuan, 611756 China; Institute of System Science and Engineering, Southwest Jiaotong University, Chengdu Sichuan, 611756 China; National United Engineering Laboratory of Intergrated and Intelligent Transportation, Southwest Jiaotong University, Chengdu Sichuan, 611756 China; National Engineering Laboratory of Integrated Transportation Big Data Application Technology, Southwest Jiaotong University, Chengdu Sichuan, 611756 China.
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