By Topic

Data mining techniques to improve no-show forecasting

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
$31 $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

4 Author(s)
Rong Zeng Cao ; IBM Res. - China, Beijing, China ; Wei Ding ; Xiang Yang He ; Hao Zhang

In order to maximum the profit of each flight, the airlines always have some over-booking in one flight. Accurate forecasts of the expected number of noshows for each flight can increase airline revenue by reducing the number of spoiled seats and the number of involuntary denied boarding at the departure gate. In this paper, we develop a combined model to predict no-show rates using historical data and specific information on the individual passengers booked on each flight. Meanwhile, we propose some data mining techniques to improve no-show forecasting. A case study and the relative performance of some methods are introduced, together with some discussion on further research.

Published in:

Service Operations and Logistics and Informatics (SOLI), 2010 IEEE International Conference on

Date of Conference:

15-17 July 2010