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Optimizing the prices for airline flight passes. / Santos, Bruno F.; Gillis, Myrthe M.D.

In: Transportation Research Procedia, Vol. 37, 2019, p. 266-273.

Research output: Contribution to journalArticleScientificpeer-review

Harvard

Santos, BF & Gillis, MMD 2019, 'Optimizing the prices for airline flight passes' Transportation Research Procedia, vol. 37, pp. 266-273. https://doi.org/10.1016/j.trpro.2018.12.192

APA

Santos, B. F., & Gillis, M. M. D. (2019). Optimizing the prices for airline flight passes. Transportation Research Procedia, 37, 266-273. https://doi.org/10.1016/j.trpro.2018.12.192

Vancouver

Santos BF, Gillis MMD. Optimizing the prices for airline flight passes. Transportation Research Procedia. 2019;37:266-273. https://doi.org/10.1016/j.trpro.2018.12.192

Author

Santos, Bruno F. ; Gillis, Myrthe M.D. / Optimizing the prices for airline flight passes. In: Transportation Research Procedia. 2019 ; Vol. 37. pp. 266-273.

BibTeX

@article{0dec7f074c344e8999df9dec4f7cd117,
title = "Optimizing the prices for airline flight passes",
abstract = "Flight pass is a new concept in which airline passengers pre-purchase a number of flights for a flat fee. This flat flee can be customized by the passenger and has an expiring date. Being an innovative concept, the industry is still lacking analytical support to define the prices for these passes. This research is aimed to fill this research gap. We propose a data-driven modeling framework that determines the value of each option per flight and that, consequently, estimates the recommended flight pass price. This is the first time in the literature that flight passes are discussed. The framework is divided in two models. First, a random forest regression is used to predict the ticket price of individual flights. Second, the flight pass prices are predicted using a Monte-Carlo simulation. The simulation is used to estimate the potential behavior of a passenger when using the flight pass. Since no reliable flight pass data was available, we make use of historical booking data from revenue management available from a major African airline to design, calibrate, and validate our models. With an average fit of 57 percent, the random forest regression algorithm can adequately predict the flight price, improving the current trial-and-error or linear regression approaches followed by most airlines. Moreover, the Monte-Carlo simulation is fast enough to support an online implementation of the proposed modeling framework to estimate flight passes prices.",
keywords = "Airlines flight passes, Monte-Carlo simulation, random forest regression, revenue management",
author = "Santos, {Bruno F.} and Gillis, {Myrthe M.D.}",
year = "2019",
doi = "10.1016/j.trpro.2018.12.192",
language = "English",
volume = "37",
pages = "266--273",
journal = "Transportation Research Procedia",
issn = "2352-1465",
publisher = "Elsevier",

}

RIS

TY - JOUR

T1 - Optimizing the prices for airline flight passes

AU - Santos, Bruno F.

AU - Gillis, Myrthe M.D.

PY - 2019

Y1 - 2019

N2 - Flight pass is a new concept in which airline passengers pre-purchase a number of flights for a flat fee. This flat flee can be customized by the passenger and has an expiring date. Being an innovative concept, the industry is still lacking analytical support to define the prices for these passes. This research is aimed to fill this research gap. We propose a data-driven modeling framework that determines the value of each option per flight and that, consequently, estimates the recommended flight pass price. This is the first time in the literature that flight passes are discussed. The framework is divided in two models. First, a random forest regression is used to predict the ticket price of individual flights. Second, the flight pass prices are predicted using a Monte-Carlo simulation. The simulation is used to estimate the potential behavior of a passenger when using the flight pass. Since no reliable flight pass data was available, we make use of historical booking data from revenue management available from a major African airline to design, calibrate, and validate our models. With an average fit of 57 percent, the random forest regression algorithm can adequately predict the flight price, improving the current trial-and-error or linear regression approaches followed by most airlines. Moreover, the Monte-Carlo simulation is fast enough to support an online implementation of the proposed modeling framework to estimate flight passes prices.

AB - Flight pass is a new concept in which airline passengers pre-purchase a number of flights for a flat fee. This flat flee can be customized by the passenger and has an expiring date. Being an innovative concept, the industry is still lacking analytical support to define the prices for these passes. This research is aimed to fill this research gap. We propose a data-driven modeling framework that determines the value of each option per flight and that, consequently, estimates the recommended flight pass price. This is the first time in the literature that flight passes are discussed. The framework is divided in two models. First, a random forest regression is used to predict the ticket price of individual flights. Second, the flight pass prices are predicted using a Monte-Carlo simulation. The simulation is used to estimate the potential behavior of a passenger when using the flight pass. Since no reliable flight pass data was available, we make use of historical booking data from revenue management available from a major African airline to design, calibrate, and validate our models. With an average fit of 57 percent, the random forest regression algorithm can adequately predict the flight price, improving the current trial-and-error or linear regression approaches followed by most airlines. Moreover, the Monte-Carlo simulation is fast enough to support an online implementation of the proposed modeling framework to estimate flight passes prices.

KW - Airlines flight passes

KW - Monte-Carlo simulation

KW - random forest regression

KW - revenue management

UR - http://www.scopus.com/inward/record.url?scp=85062230436&partnerID=8YFLogxK

U2 - 10.1016/j.trpro.2018.12.192

DO - 10.1016/j.trpro.2018.12.192

M3 - Article

VL - 37

SP - 266

EP - 273

JO - Transportation Research Procedia

T2 - Transportation Research Procedia

JF - Transportation Research Procedia

SN - 2352-1465

ER -

ID: 51997385