By Topic

Forecasting the air transport demand for passengers with neural modelling

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
K. P. G. Alekseev ; COPPE, Univ. Fed. do Rio de Janeiro, Brazil ; J. M. Seixas

The air transport industry firmly relies on forecasting methods for supporting management decisions. However, optimistic forecasting has resulted in serious problems to the Brazilian industry in the past years. In this paper, models based on artificial neural networks are developed for the air transport passenger demand forecasting. It is found that neural processing can outperform the traditional econometric approach used in this field and can accurately generalise the learning time series behaviour, even in practical conditions, where a small number of data points is available. Feeding the input nodes of the neural estimator with pre-processed data, the forecasting error is evaluated to be smaller than 0.6%.

Published in:

Neural Networks, 2002. SBRN 2002. Proceedings. VII Brazilian Symposium on

Date of Conference:

2002