Forecasting Australia’s domestic low cost carrier passenger demand using a genetic algorithm approach
Document Type
Journal Article
Publication Title
Aviation
Publisher
Taylor & Francis
School
School of Engineering
RAS ID
22422
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. © 2016 Vilnius Gediminas Technical University (VGTU) Press.
DOI
10.3846/16487788.2016.1171798
Access Rights
free_to_read
Comments
Srisaeng, P., Richardson, S., Baxter, G. S., & Wild, G. (2016). Forecasting Australia’s domestic low cost carrier passenger demand using a genetic algorithm approach. Aviation, 20(2), 39-47. Available here.