Forecasting the international air passengers of Iran using an artificial neural network

Farzin Nourzadeh, Sadoullah Ebrahimnejad, Kaveh Khalili-Damghani and Ashkan Hafezalkotob

International Journal of Industrial and Systems Engineering, 2020, vol. 34, issue 4, 562-581

Abstract: Forecasting passenger demand is generally viewed as the most crucial function of airline management. In order to organise the air passengers entering Iran, in this study, the number of international air passengers entering Iran in 2020 has been forecast using an artificial neural network. For this purpose, first, countries that have a similar status to Iran on some indicators, have been recognised by using 11 indices. Afterward, the number of their air passengers has been forecast by using various training algorithms. Then, the number of international passengers entering Iran has been forecast using the weighted average and similarity percentage of other countries in defined indices. It should be noted that training algorithms for countries have been chosen based on experimental error, and the prediction accuracy has been set at 99% of confidence interval. Comparison of the results obtained from present study and other studies shows high accuracy of the proposed approach.

Keywords: forecasting; artificial neural network; ANN; training algorithm; air passenger demand; Iran. (search for similar items in EconPapers)
Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:ids:ijisen:v:34:y:2020:i:4:p:562-581

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