Resilience Analysis of Airport Systems Based on Improved Bayesian Networks

Modern airports, as pivotal nodes in global transportation networks, face escalating resilience challenges from compound threats, including extreme weather events and cyberattacks.However, current assessment methods primarily rely on subjective evaluations and lack probabilistic reasoning to account for the dynamic interdependencies among resilience factors. To bridge this gap, this study presents a hybrid Bayesian Network–Best Worst Method (BN-BWM) framework to enhance the accuracy and practicality of airport system resilience assessments. Although Bayesian Networks effectively model complex probabilistic dependencies, expert-based probability assignments in practice often introduce subjectivity. To overcome this limitation, we employ the Best Worst Method (BWM) for its systematic pairwise comparison approach. Building on this, we leverage BWM's systematic pairwise comparisons—conducted with 10 aviation experts—to generate conditional probability tables for the Bayesian Network.The results indicate that large airports exhibit higher resilience levels (84%–85%), while medium-sized airports display moderate resilience (79%).Sensitivity analysis identifies key factors influencing resilience, including emergency repair systems and personnel capabilities, thereby offering actionable insights to improve airport operations.This study provides a robust, data-driven framework that enhances the objectivity and accuracy of resilience evaluations, thereby offering theoretical evidence for sustainable airport management and operational safety.