An expert generation model for accident analysis based on text data

Abstract

Abstract As the narrative part to extend the accident-related content, text data can help understand the mechanism of accidents, which is always neglected in quantitative accident analysis. This paper presents a novel expert generation model to extract incidents from text reports for accident analysis. By using expert knowledge to complete the text labeling work, this paper uses a supervised text mining method to train the expert generation model. Then, the pre-trained language model is used to train the model for incident and relationship extraction. An application based on text dataset of Beijing metros is implemented to certify the effectiveness of the proposed methods. Two lines in Beijing metro are analyzed and the results of path analysis model show that different mechanism exists in the two lines, indicating that the high-order information extracted from text data is significant for understanding the incident, accident analysis, and accident prevention.