CFP last date
01 July 2024
Call for Paper
August Edition
CAE solicits high quality original research papers for the upcoming August edition of the journal. The last date of research paper submission is 01 July 2024

Submit your paper
Know more
Reseach Article

A study on Air Passenger demand Forecasting from Egypt to Suadi Arabia

by M.M. Mohie El-Din, N. I. Ghali, A. Sadek, A. A. Abouzeid
Communications on Applied Electronics
Foundation of Computer Science (FCS), NY, USA
Volume 3 - Number 1
Year of Publication: 2015
Authors: M.M. Mohie El-Din, N. I. Ghali, A. Sadek, A. A. Abouzeid
10.5120/cae2015651868

M.M. Mohie El-Din, N. I. Ghali, A. Sadek, A. A. Abouzeid . A study on Air Passenger demand Forecasting from Egypt to Suadi Arabia. Communications on Applied Electronics. 3, 1 ( October 2015), 1-5. DOI=10.5120/cae2015651868

@article{ 10.5120/cae2015651868,
author = { M.M. Mohie El-Din, N. I. Ghali, A. Sadek, A. A. Abouzeid },
title = { A study on Air Passenger demand Forecasting from Egypt to Suadi Arabia },
journal = { Communications on Applied Electronics },
issue_date = { October 2015 },
volume = { 3 },
number = { 1 },
month = { October },
year = { 2015 },
issn = { 2394-4714 },
pages = { 1-5 },
numpages = {9},
url = { https://www.caeaccess.org/archives/volume3/number1/429-2015651868/ },
doi = { 10.5120/cae2015651868 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2023-09-04T19:43:15.443474+05:30
%A M.M. Mohie El-Din
%A N. I. Ghali
%A A. Sadek
%A A. A. Abouzeid
%T A study on Air Passenger demand Forecasting from Egypt to Suadi Arabia
%J Communications on Applied Electronics
%@ 2394-4714
%V 3
%N 1
%P 1-5
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This study employed the back-propagation neural network to forecast the air passenger demand from Egypt to Saudi Arabia. The factors that influence air passenger are identified, evaluated and analyzed by applying the back-propagation neural network on the annual data 2000 to 2010 by using visual gene developer package.

References
  1. Brons, M., Pels, E., Nijkamp, P., Rietveld, P., 2002. Price elasticities of demand for passenger air travel: a metaanalysis. 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. Mohie El-Din, M. M., Ghali, N. I., Sadek , A., Abouzeid, A. A., 2015. Decision Support System for Airlines Fleet Capacity Management. International Journal of Computer Applications.
  9. Masters, T., 1993. Practical Neural Network Recipes in C++. Academic Press, Boston.
  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

Airline Passenger Demand Forecasting Artificial Neural Network