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No-show Forecast Using Passenger Booking Data

Zenkert, David (2017) FMS820 20171
Mathematical Statistics
Abstract
Amadeus IT Group provide revenue management systems for the airline industry. The concept of overbooking has been known and applied within the industry since the middle of the 20th century, thus playing a large role in a revenue management prospect. The passengers booking data is something that could improve the forecasting of the rate at which passengers don’t show up or cancel their respective flights, henceforth referred to as cancellation/no-show rate. This thesis will only address the no-show part but both the concept of cancellations and no-show together are important when overbooking flights optimally. Overbooking too little will result in lost revenues and overbooking too much will result in fees for compensating possibly upset... (More)
Amadeus IT Group provide revenue management systems for the airline industry. The concept of overbooking has been known and applied within the industry since the middle of the 20th century, thus playing a large role in a revenue management prospect. The passengers booking data is something that could improve the forecasting of the rate at which passengers don’t show up or cancel their respective flights, henceforth referred to as cancellation/no-show rate. This thesis will only address the no-show part but both the concept of cancellations and no-show together are important when overbooking flights optimally. Overbooking too little will result in lost revenues and overbooking too much will result in fees for compensating possibly upset passengers and of course the issue of having to deny boarding to them as well. Therefore, the investigation around how to optimally overbook flights is of importance for Amadeus.
In this thesis, machine learning algorithms are tested with the objective to improve the no-show rates. The revenue management part of this project will not be discussed in great detail (Less)
Please use this url to cite or link to this publication:
author
Zenkert, David
supervisor
organization
course
FMS820 20171
year
type
H2 - Master's Degree (Two Years)
subject
language
English
id
8903812
date added to LUP
2017-02-27 14:11:09
date last changed
2017-02-27 14:11:09
@misc{8903812,
  abstract     = {Amadeus IT Group provide revenue management systems for the airline industry. The concept of overbooking has been known and applied within the industry since the middle of the 20th century, thus playing a large role in a revenue management prospect. The passengers booking data is something that could improve the forecasting of the rate at which passengers don’t show up or cancel their respective flights, henceforth referred to as cancellation/no-show rate. This thesis will only address the no-show part but both the concept of cancellations and no-show together are important when overbooking flights optimally. Overbooking too little will result in lost revenues and overbooking too much will result in fees for compensating possibly upset passengers and of course the issue of having to deny boarding to them as well. Therefore, the investigation around how to optimally overbook flights is of importance for Amadeus.
In this thesis, machine learning algorithms are tested with the objective to improve the no-show rates. The revenue management part of this project will not be discussed in great detail},
  author       = {Zenkert, David},
  language     = {eng},
  note         = {Student Paper},
  title        = {No-show Forecast Using Passenger Booking Data},
  year         = {2017},
}