Resilience Analysis of Airport Systems Based on Improved Bayesian Networks
Jiuxia Guo, Xin Tong, Yun‐Gui Yang, Yuan Jiang, Siyi Xu
Abstract
Modern airports, as pivotal nodes in global transportation networks, face increasing resilience challenges from compound threats such as 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 address this gap, this study presents a hybrid Bayesian network–best worst method (BN–BWM) framework aimed at improving the accuracy and practicality of airport system resilience assessments. While Bayesian networks are effective for modeling complex probabilistic dependencies, expert-based probability assignments often introduce subjectivity. To mitigate this, we apply the best worst method (BWM) to conduct systematic pairwise comparison. Building on this, we leverage the 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 demonstrate higher resilience levels (84–85%), whereas medium-sized airports exhibit moderate resilience (79%). Sensitivity analysis identifies key factors influencing resilience, including emergency repair systems and personnel capabilities, thereby offering actionable insights into improving airport operations. This study presents a robust, data-driven framework that enhances the objectivity and accuracy of resilience evaluations, providing theoretical support for sustainable airport management and operational safety.