A Mathematical Model on the Temporal Dynamics of Aviation Competitive Pricing

This study investigates the competitive dynamics of airport pricing using U.S. airport data to validate the findings. It employs linear and nonlinear ordinary differential equation models to analyze the influence of competitive interactions and internal factors on pricing decisions. The methodology involves parameter estimation via optimization techniques and quantile regression to capture heterogeneity across market segments. Mathematical analysis and simulation results show that if competitive coupling coefficients are low then there is weak competitive influence on pricing, with airports’ pricing largely driven by internal factors. Also, if the adjustment rates exhibit consistency across airports then internal dynamics are dominant in price adjustments. Using the US airports data, quantile regression further reveals that competitive effects become more pronounced in premium market segments, with variations across quantiles. The findings suggest that airport pricing strategies should focus on internal factors rather than competition. This study recommends integrating demand elasticity, capacity constraints, and stochastic elements to refine pricing models and enhance market efficiency.