Citation

Xiaobing D, Gan S, Zhigang L, Hua H. Urban Rail Transit 2023; 9(2): 147-178.

Copyright

(Copyright © 2023, Holtzbrinck Springer Nature Publishing Group)

DOI

10.1007/s40864-023-00192-3

PMID

unavailable

Abstract

With the promotion of the major strategy of national transportation power, the super-large-scale metro operation network has become an inevitable trend, and operation safety has become increasingly prominent. Metro operation dispatch logs and accident reports were taken as the research object, the hazard sources were efficiently and accurately identified, the risk chains of hazard sources were mined, and the risk evolution mechanism was revealed. Firstly, the transportation lexicon was constructed to improve the accuracy of word segmentation, the text features were extracted based on the term frequency-inverse document frequency (TF-IDF) algorithm, and the key eigenvalues were mined to identify the key hazard sources. Secondly, pattern matching was used to extract explicit causality, and the Self-Attention BiLSTM extracted implicit causality and integrated event trigger word position identification to enhance the effectiveness of implicit causality extraction. Finally, an example was given to verify the efficiency and accuracy of the model, the experiments showed that the F value of extraction effect was increased by nearly 10%, the extraction accuracy was 87.53%, the identification of operational hazard sources was more accurate, and the visualization of risk evolution law was realized.

Language: en

Keywords

Causality extraction; Operational hazards; Risk chain; Text mining; Urban rail transit