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.
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This paper was supported by Beijing Logistic Informatics Research Base, China. We appreciate their support very much.
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Chang, D., Zhang, M., Gong, D. (2023). Importance Evaluation of Factors for the Railway Accidents Based on TF-K. In: Li, M., Hua, G., Fu, X., Huang, A., Chang, D. (eds) IEIS 2022. ICIEIS 2022. Lecture Notes in Operations Research. Springer, Singapore. https://doi.org/10.1007/978-981-99-3618-2_7
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