Correlation Analysis of External Environment Risk Factors for High-Speed Railway Derailment Based on Unstructured Data

In railway operation, unsafe events such as faults may occur, and a large number of unsafe event records are generated in the process of unsafe events’ recording and reporting. Unsafe events have been described in unstructured natural language, which often has inconsistent structure and complex sources, involving multiple railway specialties, with multisource, heterogeneous, and unstructured characteristics. In practical application, the efficiency of processing is extremely low, leading to potentially unsafe management utilization. Based on the data on unsafe events, this paper utilizes big data processing technology, conducts association rules mining and association degree analysis, extracts the word segmentation, and obtains the feature vector of unsafe fault event data. At the same time, the unsafe event data analysis model is constructed in combination with regular expression and pattern matching technology. This paper establishes the matching model of high-speed railway derailment-based external environment risk factors and applies it to the occurrence of unsafe events. This model could be utilized to analyze and excavate the link between external environment risk factors and the occurrence of unsafe events and carry out the automatic extraction of characteristic information such as risk possibility and consequence severity; hence, it has potential for identifying, with enhanced accuracy, high-risk factors that may lead to high-speed railway derailment. Based on this study, we could make full use of the unsafe event data, forecast the risk trend, and discover the law of high-speed railway derailment. This study introduces a viable approach to analyzing the unsafe event data, forecasting risk trend, and conceptualizing high-speed railway derailment. It could also enforce the accurate quantification of high-speed railway safety situation, refine the risk index and conduct in-depth analysis combined with the model, and effectively support the digitalization and intellectualization of high-speed railway operation safety.

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