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

被引：2

作者：

Nourzadeh F. ^{[1
]}

Ebrahimnejad S. ^{[2
]}

Khalili-Damghani K. ^{[1
]}

Hafezalkotob A. ^{[1
]}

机构：

[1] Department of Industrial Engineering, South Tehran Branch, Islamic Azad University, Tehran

[2] Department of Industrial Engineering, Karaj Branch, Islamic Azad University, P.O. Box 31485/313, Karaj

来源：

International Journal of Industrial and Systems Engineering | 2020年 / 34卷 / 04期

关键词：

Air passenger demand; ANN; Artificial neural network; Forecasting; Iran; Training algorithm;

D O I：

10.1504/IJISE.2020.106089

中图分类号：

学科分类号：

摘要：

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. Copyright © 2020 Inderscience Enterprises Ltd.

引用

页码：562 / 581

页数：19

共 21 条

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