Machine Learning for Air Transport Planning and Management

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dc.contributor.authorGraham Wild
dc.contributor.authorGlenn Baxter
dc.contributor.authorPannarat Srisaeng
dc.contributor.authorSteven Richardson
dc.date.accessioned2025-03-10T07:35:06Z
dc.date.available2025-03-10T07:35:06Z
dc.date.issued2022
dc.description.abstractIn this work we compare the performance of several machine learning algorithms applied to the problem of modelling air transport demand. Forecasting in the air transport industry is an essential part of planning and managing because of the economic and financial aspects of the industry. The traditional approach used in airline operations as specified by the International Civil Aviation Organization is the use of a multiple linear regression (MLR) model, utilizing cost variables and economic factors. Here, the performance of models utilizing an artificial neural network (ANN), an adaptive neuro-fuzzy inference system (ANFIS), a genetic algorithm, a support vector machine, and a regression tree are compared to MLR. The ANN and ANFIS had the best performance in terms of the lowest mean squared error. © 2022, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.
dc.identifier.citationAIAA AVIATION 2022 Forum
dc.identifier.doi10.2514/6.2022-3706
dc.identifier.isbn978-162410635-4
dc.identifier.scopus2-s2.0-85135066221
dc.identifier.urihttps://repository.dusit.ac.th//handle/123456789/4641
dc.languageEnglish
dc.publisherAmerican Institute of Aeronautics and Astronautics Inc, AIAA
dc.rightsAll Open Access; Green Open Access
dc.rights.holderScopus
dc.titleMachine Learning for Air Transport Planning and Management
dc.typeConference paper
mods.location.urlhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85135066221&doi=10.2514%2f6.2022-3706&partnerID=40&md5=9a0f99657a718c9dc70fe5fa64447925
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