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.

The escalating complexity of airport surface operations challenges traditional risk quantification methods. Conventional linear models often fail to capture the non-linear interactions within sociotechnical systems. While hybrid System-Theoretic Process Analysis (STPA) and Bayesian Network (BN) models provide an alternative, existing integrations are frequently constrained by ad hoc structural translations and rare-event data sparsity. To address these methodological limitations, this study proposes an enhanced STPA-BN framework. A formalized mapping mechanism (M1–M4) translates qualitative STPA scenarios into a BN topology to quantify non-linear causal dependencies across environmental precursors, operator cognitive states, unsafe control actions, and systemic hazards. Parameterization is achieved via a logic-guided strategy, fusing historical incident data mining with deterministic physical constraints to correct rare-event probabilities. The framework is validated through a reconstruction of the 2023 Austin–Bergstrom runway incursion incident. Results indicate that under low visibility and degraded surveillance, incursion probability escalates to 86%. Sensitivity analysis reveals that while restoring surveillance infrastructure reduces collision risk by ~13%, communication compliance improvements prove insufficient in sensory-deprived environments. These findings quantitatively demonstrate that administrative controls cannot substitute for robust engineering safeguards in complex operations.

Runway incursions (RIs) have emerged as a major threat to airport surface safety, driven by the coupled influence of human, equipment, environmental, and management factors. Conventional assessment methods struggle to simultaneously capture the fuzziness of expert linguistic judgment and the randomness of operational conditions. This study proposes an integrated DEMATEL–Cloud–TOPSIS framework for runway incursion risk assessment and validates it at Chengdu Tianfu International Airport. A hierarchical indicator system comprising 24 indicators across four dimensions—Human (H), Equipment (M), Environment (E), and Management (G)—was constructed from 90 RI cases collected between 2018 and 2023. DEMATEL quantified inter-indicator causal dependencies and DEMATEL-derived weights; the Cloud model translated linguistic expert judgments into digital characteristics (Ex, En, He); and TOPSIS produced relative closeness coefficients for risk ranking. Human, equipment, and environmental risks are all at a medium-risk, while management risk is at a low-risk, but significant differences still exist. Management achieved the highest closeness ($C_i$ = 0.6322) and Environment the lowest ($C_i$ = 0.5096). At the indicator level, ATC Instruction Accuracy (H1) exhibited the greatest operational maturity ($C_i$ = 0.9119), whereas Unclear Crew Coordination (H6) showed the lowest relative closeness ($C_i$ = 0.0156), followed by Aircraft Equipment (M5) ($C_i$ = 0.0195). Meanwhile, Runway Configuration Complexity (E2) remained a weak structural factor within the Environmental dimension ($C_i$ = 0.1502). The framework provides an interpretable, quantitative basis for targeted safety management at complex hub airports.

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.

Existing runway incursion risk approaches can estimate risk under specified conditions but have limited ability to represent how runway protection constraints weaken across operational stages. This study develops an integrated System-Theoretic Process Analysis (STPA)–Dynamic Bayesian Network (DBN) framework to examine how safety-constraint degradation propagates across predefined operational stages toward runway incursion risk. A Dynamic Causal Translation Logic (DCTL) provides principle-guided organization of STPA-derived artifacts into a traceable single-slice Bayesian Network (BN), which is subsequently extended across semantic stages through selected inter-slice transitions and sequential evidence updating. The model was parameterized using 66 manually coded runway incursion-related reports from the Aviation Safety Reporting System (ASRS). Intra-slice conditional probability tables (CPTs) were estimated and calibrated from these reports, while transition CPTs used expert-informed first-order Markov assumptions. The 2023 runway incursion event at John F. Kennedy International Airport (JFK) was used as a single case-informed demonstration of staged inference and does not constitute independent or cross-case validation. Posterior updating indicated weakened unobserved monitoring and verification constraints, higher unsafe control action likelihood, and sustained tendencies toward runway occupancy conflict and Category B severity. Scenario-based analysis showed that alternative safety-constraint states produced distinct local changes in downstream hazard and Category B posterior probabilities under their respective stage-specific conditioning settings. The framework supports tracing safety-constraint degradation and stage-specific runway safety management.