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Prediction of New Itinerary Markets for Airlines via Network Embedding

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Analysis of Images, Social Networks and Texts (AIST 2019)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1086))

Abstract

Network planning is one of the most significant problems that airlines solve every day. Currently, airlines utilise traveller decision choice modelling, which has certain drawbacks. It analyses each market independently, which does not consider the entire Airline network information with its dynamic structure formation based on competition factors.

In the paper, we show that Airline network structure provides an accurate prediction for the current network and for future lines. We compare several approaches for Airline network link prediction via structural network embeddings, which are interpreted as new itinerary markets estimation.

The work of I. Makarov was supported by the Russian Science Foundation under grant 17-11-01294 and performed at National Research University Higher School of Economics, Russia. The work of D. Kiselev was prepared within the framework of the HSE University Basic Research Program and funded by the Russian Academic Excellence Project ‘5-100’. Sections 1–3 were prepared by I. Makarov. Sections 4, 6 were prepared by D. Kiselev. Section 5 was contributed jointly and equally by both authors.

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Author information

Authors and Affiliations

  1. National Research University Higher School of Economics, Moscow, Russia

    Dmitrii Kiselev & Ilya Makarov

  2. Faculty of Computer and Information Science, University of Ljubljana, Ljubljana, Slovenia

    Ilya Makarov

Authors
  1. Dmitrii Kiselev
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  2. Ilya Makarov
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Corresponding author

Correspondence to Ilya Makarov .

Editor information

Editors and Affiliations

  1. RWTH Aachen University, Aachen, Germany

    Wil M. P. van der Aalst

  2. University of Ljubljana, Ljubljana, Slovenia

    Vladimir Batagelj

  3. National Research University Higher School of Economics, Moscow, Russia

    Dmitry I. Ignatov

  4. Institute of Mathematics and Mechanics Yekaterinburg, Yekaterinburg, Russia

    Michael Khachay

  5. National Research University Higher School of Economics, Moscow, Russia

    Valentina Kuskova

  6. University of Oslo, Oslo, Norway

    Andrey Kutuzov

  7. National Research University Higher School of Economics, Moscow, Russia

    Sergei O. Kuznetsov

  8. National Research University Higher School of Economics, Moscow, Russia

    Irina A. Lomazova

  9. Moscow State University, Moscow, Russia

    Natalia Loukachevitch

  10. Loria, Vandoeuvre lès Nancy, France

    Amedeo Napoli

  11. University of Florida, Gainesville, USA

    Panos M. Pardalos

  12. Ca' Foscari University of Venice, Venezia Mestre, Italy

    Marcello Pelillo

  13. National Research University Higher School of Economics, Nizhny Novgorod, Russia

    Andrey V. Savchenko

  14. Kazan Federal University, Kazan, Russia

    Elena Tutubalina

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Kiselev, D., Makarov, I. (2020). Prediction of New Itinerary Markets for Airlines via Network Embedding. In: van der Aalst, W., et al. Analysis of Images, Social Networks and Texts. AIST 2019. Communications in Computer and Information Science, vol 1086. Springer, Cham. https://doi.org/10.1007/978-3-030-39575-9_32

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  • DOI: https://doi.org/10.1007/978-3-030-39575-9_32

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