Application of text mining and causal analysis for risk identification in air traffic control safety operations

Risk assessment in air traffic control (ATC) operations is a core element in ensuring flight safety. Addressing the limitations of current assessment techniques in deeply identifying critical risks within ATC procedures, this study proposes a method that integrates text mining with causal analysis for comprehensive risk identification. First, the BERTopic model is applied to conduct deep semantic analysis of ATC-related safety occurrence reports, extracting key risk themes and their representative keywords with enhanced semantic coherence and diversity. Second, by incorporating domain-specific operational knowledge and extracted keywords, a structured Risk Factor Representation Framework is established. Event chains are annotated to construct a Causal Network for ATC Operational Safety. Finally, topological analysis (including PageRank and betweenness centrality) and vulnerability analysis are employed to identify critical risk nodes within the network. Using incident reports from China's civil aviation ATC operations in 2021 as a case study, the results demonstrate that the proposed method significantly improves the accuracy of risk theme identification and highlights the prominent role of nodes with high PageRank and centrality in system safety. By transforming fragmented textual information into a structured risk association network, this study enables a shift from scattered factor identification to systematic causal analysis. It offers a novel approach for precise hazard detection and timely risk mitigation, contributing to enhanced civil aviation safety governance.