Unsafe behavior among air traffic controllers is a significant causal factor in civil aviation safety incidents. To explore the risks and pathways associated with controller-induced aviation accidents, this study develops an analytical model of controller unsafe behavior based on association rules and fault tree Bayesian networks. First, the Human Factors Analysis and Classification System (HFACS) was applied to identify and categorize aviation incident reports attributed to controller errors. Next, association rule algorithms were employed to uncover potential associations between controller unsafe behaviors and related risk factors, and a fault tree Bayesian network (FT-BN) model of controller unsafe behaviors was constructed based on these associations. The results revealed that the most likely unsafe behaviors were: improper allocation of aircraft spacing (30.5%), failure to take necessary intervention measures (28.4%), and improper transfer of control (27.8%). Backward analysis of the FT-BN indicated that improper allocation of aircraft spacing was most likely triggered by failure to provide adequate controller training, failure to take necessary intervention measures was most often caused by forgotten information, and improper transfer of control was most frequently associated with controller fatigue and failure to put risk management efforts in place. This study provides an important framework for the analysis and evaluation of controller behavior management and offers key insights for improving air traffic safety.

Runway incursions remain a major threat to airport surface safety, and effective prevention depends on the accurate identification of causal risk factors and their interaction mechanisms. However, existing studies have mainly focused on isolated risk factors or static causal relationships, offering limited insight into the hierarchical structure and dynamic propagation of runway incursion risk in complex operational environments. To address this gap, this study proposes a quantitative framework for runway incursion risk analysis by integrating grounded theory and complex network theory. Published runway incursion cases in the Chinese civil aviation system from 2022 to 2025 were systematically analyzed through open coding, axial coding, and selective coding, resulting in a hierarchical indicator system comprising five main categories, twelve subcategories, and 112 risk indicators. Based on this system, a runway incursion causal network was constructed to characterize the causal interdependencies among risk factors. Node importance was evaluated using a TOPSIS-based multi-criteria method integrating multiple network metrics, and a load-distribution-based propagation mechanism was introduced to quantify the risk propagation probability and risk propagation intensity of each node. The results indicate that insufficient night lighting (N99), taxi-route memory errors (N14), ambiguous controller instructions (N1), and excessive controller workload (N10) exhibit relatively high risk propagation probability and risk propagation intensity, indicating their critical roles in the evolution and cascading propagation of runway incursion risk. These findings demonstrate that the proposed framework can effectively capture both the structural importance and propagation characteristics of causal risk factors. Therefore, this study provides quantitative support for understanding runway incursion risk evolution and for developing targeted prevention strategies and post-incident response measures to improve runway safety management.