Sciweavers

Share
KDD
2001
ACM

Data mining techniques to improve forecast accuracy in airline business

4 years 9 months ago
Data mining techniques to improve forecast accuracy in airline business
Predictive models developed by applying Data Mining techniques are used to improve forecasting accuracy in the airline business. In order to maximize the revenue on a flight, the number of seats available for sale is typically higher than the physical seat capacity (overbooking). To optimize the overbooking rate, an accurate estimation of the number of no-show passengers (passengers who hold a valid booking but do not appear at the gate to board for the flight) is essential. Currently~ no-shows on future flights are estimated from the number of no-shows on historical flights averaged on booking class level. In this work, classification trees and logistic regression models are applied to estimate the probability that an individual passenger turns out to be a no-show. Passenger information stored in the reservation system of the airline is either directly used as explanatory variable or used to create attributes that have an impact on the probability of a passenger to be a no-show. The ...
Christoph Hueglin, Francesco Vannotti
Added 30 Nov 2009
Updated 30 Nov 2009
Type Conference
Year 2001
Where KDD
Authors Christoph Hueglin, Francesco Vannotti
Comments (0)
books