Forecasting Australia’s Domestic Low Cost Carrier Passenger Demand Using a Genetic Algorithm Approach

Abstract

This study has proposed and empirically tested for the first time Genetic Algorithm (GA) models for forecasting Australia’s domestic low cost carriers’ demand, as measured by enplaned passengers (GAPAXDE Model) and revenue passenger kilometres performed (GARPKSDE Model). Data was divided into training and testing data sets, 36 training data sets were used to estimate the weighting factors of the GA models and 6 data sets were used for testing the robustness of the GA models. The genetic algorithm parameters used in this study comprised population size (n): 1000, the generation number: 200, and mutation rate: 0.01. The modelling results have shown that both the linear GAPAXDE and GARPKSDE models are more accurate, reliable, and have a slightly greater predictive capability compared to the quadratic models. The overall mean absolute percentage error (MAPE) of the GAPAXDE and GAR-PKSDE models are 3.33 per cent and 4.48 per cent, respectively.

Machine Learning for Air Transport Planning and Management

“…In related work, the authors further developed two GA-based models -GAPAXDE Model (enplaned passengers) and GARPKSDE Model (revenue passenger kilometres performed). The modelling results showed that both the linear GAPAXDE and GARPKSDE models were more accurate, reliable, and offered a slightly greater predictive capability as compared to the quadratic models [28]. Mohie-Eldin et al [29] used an ANN and a genetic algorithm approach to forecast the air passenger demand in Egypt (International and Domestic passengers).…”

“…Previous work has looked at modeling the LCC traffic demand in Australia [22,28,31]. As the goal of this work is not to specifically model and forecast a specific key aviation parameter with a specific machine learning tool; rather, it is to compare different machine learning tools for a given aviation parameter.…”