The forecasting of air transport passenger demands in Turkey by using novel meta-heuristic algorithms

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dc.authoridKORKMAZ, Ersin/0000-0003-3725-164X
dc.contributor.authorKorkmaz, Ersin
dc.contributor.authorAkgungor, Ali Payidar
dc.date.accessioned2025-01-21T16:45:32Z
dc.date.available2025-01-21T16:45:32Z
dc.date.issued2021
dc.departmentKırıkkale Üniversitesi
dc.description.abstractThe imbalance between modes of transport in our country appears as the most important problem. Therefore, in air transportation, which has a significant increasing trend, estimating the passenger demand with directly related parameters and novel algorithms is important for Turkey. In this study, different prediction models were developed applying for the first time with five different meta-heuristic algorithms which are Flower Pollination Algorithm (FPA), Artificial Bee Colony Algorithm (ABC), Crow Search Algorithm (CSA), Krill Herd Algorithm (KH), and the Butterfly Optimization Algorithm (BOA) to estimate Turkey's air transport demand. While developing the models, Fuel Price, Gross Domestic Product per Capita, Seat Capacity, and Annual Fuel Consumption were selected as the model parameters. Although each model developed using different approaches is applicable, quadratic and power models developed using CSA showed the highest performance. For this reason, future projections were based on these models. Air transport passenger demand was examined using two scenarios in a process until 2035. In the first scenario, according to model forms, Turkey's future air transport passenger demand will reach about 460 and 490 million passengers, respectively. In the second scenario, the number of passengers will reach approximately 375 and 660 million for quadratic and power models, respectively. The results of this study will contribute to the evaluation of the current investment plans and the development of strategic plans that will meet the demands. Additionally, they will help take necessary measures and introduce some necessary regulations to ensure the income and expense balance so that the efficiency of airline companies can be improved.
dc.identifier.doi10.1002/cpe.6263
dc.identifier.issn1532-0626
dc.identifier.issn1532-0634
dc.identifier.issue16
dc.identifier.scopus2-s2.0-85102614617
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1002/cpe.6263
dc.identifier.urihttps://hdl.handle.net/20.500.12587/25670
dc.identifier.volume33
dc.identifier.wosWOS:000629893900001
dc.identifier.wosqualityQ3
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherWiley
dc.relation.ispartofConcurrency and Computation-Practice & Experience
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_20241229
dc.subjectair transport passenger demand; artificial bee colony algorithm; butterfly optimization algorithm; crow search algorithm; flower pollination algorithm; krill herd algorithm
dc.titleThe forecasting of air transport passenger demands in Turkey by using novel meta-heuristic algorithms
dc.typeArticle

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