TY - JOUR
T1 - Modeling and Predicting Passenger Load Factor in Air Transportation
T2 - A Deep Assessment Methodology with Fractional Calculus Approach Utilizing Reservation Data
AU - Şimşek, Kevser
AU - Tuğrul, Nisa Özge Önal
AU - Karaçuha, Kamil
AU - Tabatadze, Vasil
AU - Karaçuha, Ertuğrul
N1 - Publisher Copyright:
© 2024 by the authors.
PY - 2024/4
Y1 - 2024/4
N2 - 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.
AB - 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.
KW - LF prediction
KW - air transportation
KW - deep assessment methodology
KW - fractional analysis in engineering problems
KW - fractional calculus
KW - linear regression analysis
UR - http://www.scopus.com/inward/record.url?scp=85191323291&partnerID=8YFLogxK
U2 - 10.3390/fractalfract8040214
DO - 10.3390/fractalfract8040214
M3 - Article
AN - SCOPUS:85191323291
SN - 2504-3110
VL - 8
JO - Fractal and Fractional
JF - Fractal and Fractional
IS - 4
M1 - 214
ER -