Stage-Structured Inference of Runway Incursion Risk Through Safety-Constraint Degradation: An Integrated STPA-DBN Framework for Airport Surface Operations

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.