A forecasting tool for predicting Australia�s domestic airline passenger demand using a genetic algorithm

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dc.contributor.authorPanarat Srisaeng
dc.contributor.authorGlenn Baxter
dc.contributor.authorSteven Richardson
dc.contributor.authorGraham Wild
dc.contributor.correspondenceG. Wild; RMIT University, School of Aerospace, Mechanical, and Manufacturing Engineering, Melbourne, 124 La Trobe Stm, 3000, Australia; email: graham.wild@rmit.edu.au
dc.date.accessioned2025-03-10T07:36:30Z
dc.date.available2025-03-10T07:36:30Z
dc.date.issued2015
dc.description.abstractThis study has proposed and empirically tested for the first time genetic algorithm optimization models for modelling Australia�s domestic airline passenger demand, as measured by enplaned passengers (GAPAXDE model) and revenue passenger kilometres performed (GARPKSDE model). Data was divided into training and testing datasets; 74 training datasets were used to estimate the weighting factors of the genetic algorithm models and 13 out-of-sample datasets were used for testing the robustness of the genetic algorithm models. The genetic algorithm parameters used in this study comprised population size (n): 200; the generation number: 1,000; and mutation rate: 0.01. The modelling results have shown that both the quadratic GAPAXDE and GARPKSDE models are more accurate, reliable, and have greater predictive capability as compared to the linear models. The mean absolute percentage error in the out of sample testing dataset for the GAPAXDE and GARPKSDE quadratic models are 2.55 and 2.23%, respectively. � 2015, Journal of Aerospace Technology and Management. All Rights Reserved.
dc.identifier.citationJournal of Aerospace Technology and Management
dc.identifier.doi10.5028/jatm.v7i4/475
dc.identifier.issn19849648
dc.identifier.scopus2-s2.0-84949514894
dc.identifier.urihttps://repository.dusit.ac.th//handle/123456789/4839
dc.languageEnglish
dc.publisherDepartamento de Ciencia e Tecnologia Aeroespacial
dc.rights.holderScopus
dc.subjectAir transport
dc.subjectAustralia
dc.subjectDomestic airlines
dc.subjectForecasting method
dc.subjectGenetic algorithm
dc.titleA forecasting tool for predicting Australia�s domestic airline passenger demand using a genetic algorithm
dc.typeArticle
mods.location.urlhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-84949514894&doi=10.5028%2fjatm.v7i4%2f475&partnerID=40&md5=028b491cc4501b7deae52080898d47a9
oaire.citation.issue4
oaire.citation.volume7
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