Predicting Australia’s Domestic Airline Passenger Demand using an Anfis Approach

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dc.contributor.authorPanarat Srisaeng
dc.contributor.authorGlenn Baxter
dc.date.accessioned2025-03-10T04:10:27Z
dc.date.available2025-03-10T04:10:27Z
dc.date.issued2022-04-30
dc.description.abstractThe forecasting of future airline passenger demand is critical task for airline management. The objective of the present study was to develop an adaptive neuro-fuzzy inference system (ANFIS) for predicting Australia's domestic airline passenger demand. The ANFIS model was trained, tested, and validated in the study. Sugeno fuzzy rules were used in the ANFIS structure and Gaussian membership function, and linear membership functions were also developed. The hybrid learning algorithm and the subtractive clustering partition method were used to generate the optimum ANFIS models. The results found that the mean absolute percentage error (MAPE) for the overall data set of the ANFIS model was 3.25% demonstrating that the ANFIS model has high predictive capabilities. The ANFIS model could be used in other domestic air travel markets. © 2022 Panarat Srisaeng et al., published by Sciendo.
dc.identifier.doi10.2478/ttj-2022-0013
dc.identifier.issn14076160
dc.identifier.urihttps://repository.dusit.ac.th//handle/123456789/4426
dc.language.isoen
dc.publisherSciendo
dc.relation.ispartofseriesVolume 23, Issue 2, Pages 151 - 159
dc.subjectadaptive neuro-fuzzy inference system (ANFIS)
dc.subjectAustralia
dc.subjectforecasting methods
dc.subjectdomestic airlines
dc.subjectpassenger forecasting
dc.titlePredicting Australia’s Domestic Airline Passenger Demand using an Anfis Approach
dc.typeArticle
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