Railroad accident causal analysis with unstructured narratives using bidirectional encoder representations for transformers

IF 2.4 3区 工程技术 Q3 TRANSPORTATION Journal of Transportation Safety & Security Pub Date : 2022-10-07 DOI:10.1080/19439962.2022.2128956
Bingxue Song, Xiaoping Ma, Yong Qin, Hao Hu, Zhipeng Zhang
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引用次数: 4

Abstract

Abstract Railroad safety is one critical concern for the railroad industry. Extracting useful information from railroad safety-related textual materials is one significant and essential task. To better understand the contributing factors to the railroad accidents, previous studies have primarily focused on the structured fields, while few of them have developed a thorough analysis of the narratives. In addition, due to the difficulty of understanding the terminologies in the accidents’ narratives, it is challenging to extensively use these narratives as a time-consuming and labor-intensive task. Therefore, this study proposed a novel deep learning approach to consistently leverage the values behind these railroad accident narratives. The proposed method modified the classical Bidirectional Encoder Representations for Transformers (BERT) with the connection of a Deep Neural Network (DNN). To validate the superiority of the proposed BERT-DNN, several additional text classification methods were employed in the real-world railroad accident database. Results demonstrate the proposed method in this study can assign congruous accident causes based on the railroad accidents’ narratives precisely and outperforms previous state-of-the-art text classification approaches. The analytical results, along with proposed methodological framework, can contribute to an in-deep understanding of accident causes for practitioners and academics, and ultimately enhance rail operation safety.
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用变压器双向编码器表示的非结构化叙述的铁路事故原因分析
铁路安全是铁路行业关注的焦点之一。从铁路安全相关文本资料中提取有用信息是一项重要而必要的任务。为了更好地理解铁路事故的影响因素,以往的研究主要集中在结构化领域,而很少有研究对叙事进行深入的分析。此外,由于难以理解事故叙述中的术语,因此广泛使用这些叙述是一项耗时且劳动密集型的任务,具有挑战性。因此,本研究提出了一种新颖的深度学习方法,以始终如一地利用这些铁路事故叙事背后的价值。该方法利用深度神经网络(DNN)对经典的变压器双向编码器表示(BERT)进行了改进。为了验证BERT-DNN的优越性,在实际的铁路事故数据库中采用了几种附加的文本分类方法。结果表明,本文所提出的方法可以准确地根据铁路事故的叙述来分配一致的事故原因,并且优于先前最先进的文本分类方法。分析结果以及提出的方法框架有助于从业者和学者深入了解事故原因,并最终提高铁路运营安全。
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CiteScore
6.00
自引率
15.40%
发文量
38
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