A Unified Machine Learning Architecture for Airline Revenue Enhancement: Integrating DemandPrediction, Price Sensitivity Analysis, and Adaptive Pricing Strategies

A Unified Machine Learning Architecture for Airline Revenue Enhancement: Integrating DemandPrediction, Price Sensitivity Analysis, and Adaptive Pricing Strategies | 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 A Unified Machine Learning Architecture for Airline Revenue Enhancement: Integrating DemandPrediction, Price Sensitivity Analysis, and Adaptive Pricing Strategies Yogesh Prabhakar Pingle, Swayam Chaudhary, Atharva Rodge, Om Ghat This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8585265/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 This study presents a novel computational frame- work designed to overcome persistent obstacles in contemporary airline revenue management through the synergistic combination of demand forecasting, price elasticity modeling, and dynamic pricing optimization. The proposed system introduces three fun- damental advancements: (1) an ensemble regression methodology for passenger demand estimation attaining R 2 = 0.917 through 48 strategically engineered features encompassing temporal pat- terns, competitive dynamics, operational characteristics, and passenger behavior; (2) an innovative multi-tiered hierarchical framework for elasticity assessment that addresses data scarcity constraints, generating 1,197 statistically reliable route-specific price sensitivity parameters (mean:−1.64, standard deviation: 0.31); and (3) a cost-conscious optimization mechanism incor- porating fuel consumption and capacity allocation expenses, identifying an optimal price adjustment coefficient of 1.10× that produces 2.1%–2.5% profit improvement with 11% risk reduction as evaluated through stochastic simulation. Tested on 226,686 domestic U.S. flight records, the developed framework establishes new performance benchmarks for holistic airline revenue optimization while delivering practical implementation strategies. The hierarchical approach to data scarcity and prob- abilistic optimization methodology offer transferable solutions for revenue management under uncertainty applicable across transportation and service industries. Airline Revenue Optimization Computational Intelligence Demand Forecasting Price Elasticity Modeling Dynamic Pricing Ensemble Methods Hierarchical Estimation Stochastic Optimization Cost Integration Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 16 May, 2026 Reviews received at journal 25 Apr, 2026 Reviewers agreed at journal 24 Apr, 2026 Reviewers agreed at journal 01 Feb, 2026 Reviewers invited by journal 29 Jan, 2026 Editor assigned by journal 25 Jan, 2026 Submission checks completed at journal 25 Jan, 2026 First submitted to journal 12 Jan, 2026 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-8585265","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":577235261,"identity":"739666a4-1726-4d5a-82ee-e6f1a020052d","order_by":0,"name":"Yogesh Prabhakar Pingle","email":"","orcid":"","institution":"University of Mumbai","correspondingAuthor":false,"prefix":"","firstName":"Yogesh","middleName":"Prabhakar","lastName":"Pingle","suffix":""},{"id":577235262,"identity":"69b24127-bd93-43d7-b7bf-ce5eb7fb10fd","order_by":1,"name":"Swayam 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