Quantitative analysis of freight train derailment severity with structured and unstructured data

Bing Song, Zhipeng Zhang, Yong Qin, Xiang Liu and Hao Hu

Reliability Engineering and System Safety, 2022, vol. 224, issue C

Abstract: Train safety has been a top priority in the railroad industry. Understanding accident risks is of paramount importance for prioritizing effective prevention strategies. Previous work has focused on estimating the severity of derailments and various statistical models based on structured data were used. However, unstructured data records which provide considerable information about train derailments have received minimal consideration due to a lack of procedures of processing and interpreting them. To narrow this knowledge gap, this study aims to quantitatively estimate derailment severity by considering unstructured data utilizing topic modeling. A statistical model that integrates both structured and unstructured data was established to analyze U.S. freight train derailments from 1996 to 2019. The comparative results of predictions revealed that the model with combined text information outperformed the one without the unstructured data.Quantile regression was also developed to assess various statistical distributions of derailment severity. Both models with unstructured data provide a deeper understanding of derailment severity and ultimately improve railroad safety performance.

Keywords: Railroad safety; Derailment severity; Natural language processing; Topic modeling (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (4)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:224:y:2022:i:c:s0951832022002113

DOI: 10.1016/j.ress.2022.108563

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