IDEAS home Printed from https://ideas.repec.org/a/pal/jorapm/v20y2021i3d10.1057_s41272-021-00319-w.html
   My bibliography  Save this article

Artificial Intelligence in travel

Author

Listed:
  • B. Vinod

    (Charter and Go)

Abstract

Over the past four decades Operations Research (OR) has played a key role in solving complex problems in airline planning and operations. Over the past decade Artificial Intelligence (AI) has seen a rapid growth in adoption across a range of industry verticals such as automotive, telecommunications, aerospace, and health care. It has been acknowledged that while adoption of AI in the travel industry has been slow, the potential incremental value is high. This paper discusses the role of AI and a range of applications in travel to support revenue growth and customer satisfaction.

Suggested Citation

  • B. Vinod, 2021. "Artificial Intelligence in travel," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 20(3), pages 368-375, June.
  • Handle: RePEc:pal:jorapm:v:20:y:2021:i:3:d:10.1057_s41272-021-00319-w
    DOI: 10.1057/s41272-021-00319-w
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1057/s41272-021-00319-w
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1057/s41272-021-00319-w?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Ben Vinod & Richard Ratliff & Vikram Jayaram, 2018. "An approach to offer management: maximizing sales with fare products and ancillaries," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 17(2), pages 91-101, April.
    2. Aditya Kothari & Manini Madireddy & Ramasubramanian Sundararajan, 2016. "Discovering patterns in traveler behaviour using segmentation," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 15(5), pages 334-351, October.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Daniel Schubert & Christa Sys & Rosário Macário, 2022. "Customized airline offer management: a conceptual architecture," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 21(5), pages 553-563, October.
    2. Alberto Guerrini & Gabriele Ferri & Stefano Rocchi & Marcelo Cirelli & Vicente Piña & Antoine Grieszmann, 2023. "Personalization @ scale in airlines: combining the power of rich customer data, experiential learning, and revenue management," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 22(2), pages 171-180, April.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Manini Madireddy & Ramasubramanian Sundararajan & Goda Doreswamy & Meisam Hejazi Nia & Amod Mital, 2017. "Constructing bundled offers for airline customers," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 16(6), pages 532-552, December.
    2. Michal Sznajder & Richard Ratliff & Cuneyd Kaya, 2023. "A heuristic for incorporating ancillaries into air choice models with personalization (Part 1: estimating preferences using hedonic regression)," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 22(2), pages 122-139, April.
    3. Kevin K. Wang & Michael D. Wittman & Thomas Fiig, 2023. "Dynamic offer creation for airline ancillaries using a Markov chain choice model," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 22(2), pages 103-121, April.
    4. Stacey Mumbower & Susan Hotle & Laurie A. Garrow, 2023. "Highly debated but still unbundled: The evolution of U.S. airline ancillary products and pricing strategies," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 22(4), pages 276-293, August.
    5. Daniel Schubert & Christa Sys & Rosário Macário, 2022. "Customized airline offer management: a conceptual architecture," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 21(5), pages 553-563, October.
    6. Grażyna Rosa, 2021. "Passenger Preferences in Rail Transport in Poland as Regards Travelling Time and Cost," Sustainability, MDPI, vol. 13(9), pages 1-16, April.
    7. B. Vinod, 2021. "The age of intelligent retailing: personalized offers in travel for a segment of ONE," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 20(4), pages 473-479, August.
    8. Kevin K. Wang & Michael D. Wittman & Adam Bockelie, 2021. "Dynamic offer generation in airline revenue management," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 20(6), pages 654-668, December.
    9. MadhuSudan Rao Kummara & Bhaskara Rao Guntreddy & Ines Garcia Vega & Yun Hsuan Tai, 2021. "Dynamic pricing of ancillaries using machine learning: one step closer to full offer optimization," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 20(6), pages 646-653, December.
    10. Muzaffer Buyruk & Ertan Güner, 2022. "Personalization in airline revenue management: an overview and future outlook," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 21(2), pages 129-139, April.
    11. Michael Byrd & Ross Darrow, 2021. "A note on the advantage of context in Thompson sampling," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 20(3), pages 316-321, June.
    12. Michael D. Wittman & Peter P. Belobaba, 2018. "Customized dynamic pricing of airline fare products," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 17(2), pages 78-90, April.
    13. B. Vinod, 2021. "Advances in revenue management: the last frontier," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 20(1), pages 15-20, February.
    14. Michal Sznajder & Richard Ratliff & Cuneyd Kaya, 2023. "A heuristic for incorporating ancillaries into air choice models with personalization (part 2: integrated multinomial logit and hedonic regression models)," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 22(2), pages 140-151, April.
    15. Tomasz Szymanski & Ross Darrow, 2021. "Shelf placement optimization for air products," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 20(3), pages 322-329, June.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:pal:jorapm:v:20:y:2021:i:3:d:10.1057_s41272-021-00319-w. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.palgrave.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.