Comparison of artificial neural networks and regression analysis for airway passenger estimation

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dc.contributor.authorAri D.
dc.contributor.authorMizrak Ozfirat P.
dc.date.accessioned2024-07-22T08:01:41Z
dc.date.available2024-07-22T08:01:41Z
dc.date.issued2024
dc.description.abstractWith the increasing demand in operations, time is getting more important. In order to use time and energy more effectively, it is becoming more important for airline companies and airport managements to make strategic plans for the future. To make beneficial and correct strategic plans for airways, one of the factors that is needed to be considered is future passenger numbers. With more accurate passenger number forecasts, airport managements can act more efficiently and reduce time, energy consumption and hence would be able to reduce costs. In this study, airway passenger number estimation is handled. Three metropolitan cities’ airport passenger numbers are considered. Artificial neural networks and regression analysis are carried out to estimate passenger number. In addition, data are handled in two different ways. Firstly, ANN and regression analysis are applied using original data series. In the second step, seasonal decomposition is applied on the data series and both approaches are repeated for deseasonal series. In Artificial Neural Networks approach, an experimental design is developed considering training algorithms, number of input nodes and number of nodes in the hidden layer which make up 960 design points. In the results of these experiments, performance of ANN approach is tested for three input factors and high-performance design points are identified. Furthermore, for benchmarking purposes, regression analysis is carried out. Linear, logarithmic, power, exponential, and polynomial models are developed. Finally, results of ANN and regression approaches are compared in terms of mean absolute percent error, and it is found that ANN overperformed compared to regression analysis. © 2024 Elsevier Ltd
dc.identifier.DOI-ID10.1016/j.jairtraman.2024.102553
dc.identifier.issn09696997
dc.identifier.urihttp://akademikarsiv.cbu.edu.tr:4000/handle/123456789/11555
dc.language.isoEnglish
dc.publisherElsevier Ltd
dc.subjectair transportation
dc.subjectairline industry
dc.subjectartificial neural network
dc.subjectcomparative study
dc.subjectestimation method
dc.subjectexperimental design
dc.subjectmodel test
dc.subjectmodel validation
dc.subjectperformance assessment
dc.subjectregression analysis
dc.subjectseasonality
dc.subjecttransportation economics
dc.subjecttravel behavior
dc.subjecttravel demand
dc.titleComparison of artificial neural networks and regression analysis for airway passenger estimation
dc.typeArticle

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