Resilience Analysis of Airport Systems Based on Improved Bayesian Networks

Resilience Analysis of Airport Systems Based on Improved Bayesian Networks | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Resilience Analysis of Airport Systems Based on Improved Bayesian Networks First Jiuxia Guo, Second Xin Tong, Third Yungui Yang, Fourth Jiang Yuan, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6027406/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 8 You are reading this latest preprint version Abstract 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. Airport system Resilience evaluation Best-Worst Method (BWM) Sensitivity analysis Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 29 May, 2025 Reviews received at journal 25 Apr, 2025 Reviews received at journal 25 Apr, 2025 Reviewers agreed at journal 24 Apr, 2025 Reviewers agreed at journal 22 Apr, 2025 Reviewers invited by journal 22 Apr, 2025 Submission checks completed at journal 22 Apr, 2025 First submitted to journal 16 Apr, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6027406","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":446226974,"identity":"215ebe55-7b03-4873-ad04-a2cbe93a7d2b","order_by":0,"name":"First Jiuxia 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