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

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.