Airline ticket price and demand prediction: A survey

Juhar Ahmed Abdella, N. M. Zaki, Khaled Shuaib, Fahad Khan

Research output: Contribution to journalReview articlepeer-review

13 Citations (Scopus)

Abstract

Nowadays, airline ticket prices can vary dynamically and significantly for the same flight, even for nearby seats within the same cabin. Customers are seeking to get the lowest price while airlines are trying to keep their overall revenue as high as possible and maximize their profit. Airlines use various kinds of computational techniques to increase their revenue such as demand prediction and price discrimination. From the customer side, two kinds of models are proposed by different researchers to save money for customers: models that predict the optimal time to buy a ticket and models that predict the minimum ticket price. In this paper, we present a review of customer side and airlines side prediction models. Our review analysis shows that models on both sides rely on limited set of features such as historical ticket price data, ticket purchase date and departure date. Features extracted from external factors such as social media data and search engine query are not considered. Therefore, we introduce and discuss the concept of using social media data for ticket/demand prediction.

Original languageEnglish
Pages (from-to)375-391
Number of pages17
JournalJournal of King Saud University - Computer and Information Sciences
Volume33
Issue number4
DOIs
Publication statusPublished - May 2021

Keywords

  • Deep learning line
  • Demand prediction
  • Price discrimination
  • Social media
  • Survey
  • Ticket price prediction

ASJC Scopus subject areas

  • Computer Science(all)

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