CFP last date
20 December 2024
Call for Paper
January Edition
IJCA solicits high quality original research papers for the upcoming January edition of the journal. The last date of research paper submission is 20 December 2024

Submit your paper
Know more
The week's pick
Responsive image
Improved Shuffled Frog Leaping Algorithm with Self-Adaptive Shuffling for Fuzzy Logic PD+G Controller Optimization in Robotic Manipulators

Duc Hoang Nguyen
Random Articles
Reseach Article

Airline Passenger Forecasting in EGYPT (Domestic and International)

by M. M. Mohie El-Din, M. S. Farag, A. A. Abouzeid
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 165 - Number 6
Year of Publication: 2017
Authors: M. M. Mohie El-Din, M. S. Farag, A. A. Abouzeid
10.5120/ijca2017913113

M. M. Mohie El-Din, M. S. Farag, A. A. Abouzeid . Airline Passenger Forecasting in EGYPT (Domestic and International). International Journal of Computer Applications. 165, 6 ( May 2017), 1-5. DOI=10.5120/ijca2017913113

@article{ 10.5120/ijca2017913113,
author = { M. M. Mohie El-Din, M. S. Farag, A. A. Abouzeid },
title = { Airline Passenger Forecasting in EGYPT (Domestic and International) },
journal = { International Journal of Computer Applications },
issue_date = { May 2017 },
volume = { 165 },
number = { 6 },
month = { May },
year = { 2017 },
issn = { 0975-8887 },
pages = { 1-5 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume165/number6/27574-2017913113/ },
doi = { 10.5120/ijca2017913113 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:11:39.657939+05:30
%A M. M. Mohie El-Din
%A M. S. Farag
%A A. A. Abouzeid
%T Airline Passenger Forecasting in EGYPT (Domestic and International)
%J International Journal of Computer Applications
%@ 0975-8887
%V 165
%N 6
%P 1-5
%D 2017
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This study employed the back-propagation neural network and genetic algorithm to forecast the air passenger demand in Egypt(International and Domestic). The factors that influence air passenger are identified, evaluated and analyzed by applying the back-propagation neural network on the monthly data from 1970 to 2013 by using Matlab R2013b.

References
  1. Brons, M., Pels, E., Nijkamp, P., Rietveld, P., 2002. Price elasticities of demand for passenger air travel: a meta-analysis. Journal of Air Transport Management, 8, 165-175.
  2. Hsu, C. I., Wen, Y. H., 1998. Improved grey prediction models for the trans-Pacific air passenger market. Transportation Planning and Technology, 22, 87-107.
  3. Hsu, C. I., Wen, Y. H., 2000. Application of grey theory and multiobjective programming towards airline network design. European Journal of Operational Research, 127, 44-68.
  4. Grosche, T., Rothlauf, T., Heinzl, A., 2007. Gravity models for airline passenger volume estimation. Journal of Air Transport Management, 13, 175-183.
  5. Kuo, S. Y., Shiau, L. C., Chang, Y. P., 2010. Air transport demand forecasting in routes network by artificial neural networks. Journal of Aeronautics Astronautics and Aviation Series B, 42, 67-72. (in Chinese).
  6. BaFail, A. O., 2004. Applying data mining technologies to forecast number of airline passengers in Saudi Arabia (domestic and international travels). Journal of Air Transportation.
  7. Kuo, S. Y., chen, S. C., 2011. Air Passenger and Air Cargo Demand Forecasting: Applying Neural Network ro Evaluating Input Variables.
  8. Masters, T., 1993. Practical Neural Network Recipes in C++. Academic Press, Boston.
  9. Mohie El-Din, M. M., Ghali, N. I., Sadek , A., Abouzeid, A. A., 2015. A study on air passenger demand forecasting from Egypt to Suadi Arabia. Comunications on Applied Electronics.
  10. Lyneis, J., 2000. System dynamics for market forecasting and structural analysis. System Dynamics Review, 16, 325.
  11. Miller, B., Clarke, J. P., 2007. The hidden value of air transportation infrastructure. Technological Forecasting and Social Science, 74, 1835.
  12. Law, R., Au, N., 1999. A neural network model to forecast Japanese demand for travel to Hong Kong. Tourism Management, 20, 89-97.
Index Terms

Computer Science
Information Sciences

Keywords

Air passenger Forecasting Neural Network Genetic algorithms

Powered by PhDFocusTM