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DOI

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.

Original languageEnglish
Pages (from-to)266-273
Number of pages8
JournalTransportation Research Procedia
Volume37
DOIs
Publication statusPublished - 2019
Event21st EURO Working Group on Transportation Meeting, EWGT 2018 - Braunschweig, Germany
Duration: 17 Sep 201819 Sep 2018

    Research areas

  • Airlines flight passes, Monte-Carlo simulation, random forest regression, revenue management

ID: 51997385