A Hybrid STPA-BN Framework for Quantitative Risk Assessment of Runway Incursions: A Case Study of the Austin–Bergstrom Incident

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.