Forecasting Australia’s domestic low cost carrier passenger demand using a genetic algorithm approach

    Panarat Srisaeng Info
    Steven Richardson Info
    Glenn S. Baxter Info
    Graham Wild Info

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.

Keywords:

Australia, forecasting method, genetic algorithm (GA), low cost carriers, air transport

How to Cite

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. https://doi.org/10.3846/16487788.2016.1171798

Share

Published in Issue
June 16, 2016
Abstract Views
1014

View article in other formats

CrossMark check

CrossMark logo

Published

2016-06-16

Issue

Section

Articles

How to Cite

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. https://doi.org/10.3846/16487788.2016.1171798

Share