Importance Evaluation of Factors for the Railway Accidents Based on TF-K

Dan Chang (), Min Zhang () and Daqing Gong ()
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Dan Chang: Beijing Jiaotong University
Min Zhang: Beijing Jiaotong University
Daqing Gong: Beijing Jiaotong University

A chapter in IEIS 2022, 2023, pp 63-76 from Springer

Abstract: Abstract Rail accidents cause casualty and financial loss to society. In order to extract and identify the key factors from the accident reports more accurately, this study added the word frequency-correlation importance evaluation function(TF-K*) based on complex network on the basis of text mining, and built an importance evaluation model of factors for the railway accidents. When evaluating the importance of factors, the word frequency and the correlation between factors can be considered simultaneously. In this study, 213 railway accident reports from China and Britain were collected to analyze the cause of the accident, and the final results also verified the validity of the model.

Keywords: text mining; Complex network; Association feature; Accident factor; Importance evaluation (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:spr:lnopch:978-981-99-3618-2_7

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DOI: 10.1007/978-981-99-3618-2_7

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