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Prediction of air travel demand using a hybrid artificial neural network (ANN) with Bat and Firefly algorithms: a case study

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Abstract

During the past few decades, many researchers have studied the issue of air travel demand in different countries. On the other hand, the development of airports requires considerable space in the vicinity of cities which needs planning and huge investment. However, development of air travel through different airports will be affected by various factors such as population growth and economic development. The purpose of this study is to predict air travel demand in Iran. Data were provided by the Civil Aviation Organization of Islamic Republic of Iran from 2011 to 2015. Collected information includes airports of the country and destination cities all across the country. For this purpose, the artificial neural network (ANN) is used to predict the air travel demand by considering income elasticity and population size in each zone. Evolutionary meta-heuristic algorithms have been implemented in order to improve the performance of ANN. Bat and Firefly algorithms are new meta-heuristic algorithms which have been examined in this study. The results show that the use of these algorithms increases adaptation rate of neural network (NN) prediction with real data. The coefficient of determination increases from 0.2 up to about 0.9 while using the meta-heuristics NN. This represents the high rate of efficiency using this new method.

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Authors and Affiliations

  1. Industrial Engineering Department, Yazd University, Yazd, Iran

    Ali Mostafaeipour, Alireza Goli & Mojtaba Qolipour

Authors
  1. Ali Mostafaeipour
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  2. Alireza Goli
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  3. Mojtaba Qolipour
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Corresponding author

Correspondence to Ali Mostafaeipour.

Appendix

Appendix

See Table 8.

Table 8 Total travels in each airport of Iran in last 5 years
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Mostafaeipour, A., Goli, A. & Qolipour, M. Prediction of air travel demand using a hybrid artificial neural network (ANN) with Bat and Firefly algorithms: a case study. J Supercomput 74, 5461–5484 (2018). https://doi.org/10.1007/s11227-018-2452-0

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