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

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.