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.
Highlights
Australia’s airline industry was born on connecting regional communities to the country’s major cities (Baker, Donnet 2012)
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
Summary
Australia’s airline industry was born on connecting regional communities to the country’s major cities (Baker, Donnet 2012). Australia’s air transport industry was historically tightly controlled by the government. Reliable forecasts of air transport activity play a vital role in the planning processes of States, airports, airlines, engine and airframe manufacturers, suppliers, air navigation service providers and other relevant bodies. Despite the significance of Australia’s low cost carrier domestic airline market sector, there has been no previously reported study that has developed and empirically examined genetic algorithm-based models for forecasting Australia’s domestic low cost carrier passenger demand. The primary objective of this study is to develop new genetic algorithm-based models to forecast Australia’s LCCs passenger demand and to identify whether the GA approach is a useful tool for this application. Genetic algorithm enplaned passengers (GAPAXDE) and genetic algorithm revenue passenger kilometres performed (GARPKSDE) are proposed to forecast Australia’s LCC quarterly enplaned passengers and revenue passenger kilometres performed, respectively
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