Modeling and Predicting Passenger Load Factor in Air Transportation: A Deep Assessment Methodology with Fractional Calculus Approach Utilizing Reservation Data

• Published in: Fractal and fractional Vol. 8; no. 4; p. 214
• Main Authors: Şimşek, Kevser, Tuğrul, Nisa Özge Önal, Karaçuha, Kamil, Tabatadze, Vasil, Karaçuha, Ertuğrul
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01-04-2024
• Subjects: Advertising; Aeronautics; Air transportation; Air travel; Aircraft; Airline passengers; Airlines; Aviation; Calculus; Comparative analysis; Dams; deep assessment methodology; Epidemics; Flight; Forecasts and trends; Fractals; fractional analysis in engineering problems; Fractional calculus; LF prediction; linear regression analysis; Machine learning; Mathematical models; Model accuracy; Modelling; Numerical analysis; Optimization; Passengers; Regression analysis; Regression models; Revenue; Time series; Trends; Turkey; Variables; Turkey
• ISSN: 2504-3110; 2504-3110
• DOI: 10.3390/fractalfract8040214

This study addresses the challenge of predicting the passenger load factor (PLF) in air transportation to optimize capacity management and revenue maximization. Leveraging historical reservation data from 19 Turkish Airlines market routes and sample flights, we propose a novel approach combining deep assessment methodology (DAM) with fractional calculus theory. By modeling the relationship between PLF and the number of days remaining until a flight, our method yields minimal errors compared to traditional techniques. Through a continuous curve constructed using the least-squares approach, we enable the anticipation of future flight values. Our analysis demonstrates that the DAM model with a first-order derivative outperforms linear techniques and the Fractional Model-3 in both modeling capabilities and prediction accuracy. The proposed approach offers a data-driven solution for efficiently managing air transport capacity, with implications for revenue optimization. Specifically, our modeling findings indicate that the DAM wd model improves prediction accuracy by approximately 0.67 times compared to the DAM model, surpassing the fractional model and regression analysis. For the DAM wd modeling method, the lowest average mean absolute percentage error (AMAPE) value achieved is 0.571, showcasing its effectiveness in forecasting flight outcomes.