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Cited by 114Economic and Environmental ValuationAviation Industry Analysis and TrendsTransportation Planning and Optimization

The DOI matches its Crossref record; 25 of the 25 references it cites were found in a registry or an index.

Airline itinerary choice modeling using machine learning

Lhéritier, Alix, Bocamazo, Michael, Delahaye, Thierry, Acuna-Agost, Rodrigo·2019·Journal of Choice Modelling
DOI 10.1016/j.jocm.2018.02.002
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Abstract

Abstract Understanding how customers choose between different itineraries when searching for flights is very important for the travel industry. This knowledge can help travel providers, either airlines or travel agents, to better adapt their offer to market conditions and customer needs. This has a particular importance for pricing and ranking suggestions to travelers when searching for flights. This problem has been historically handled using Multinomial Logit (MNL) models. While MNL models offer the dual advantage of simplicity and readability, they lack flexibility to handle collinear attributes and correlations between alternatives. Additionally, they require expert knowledge to introduce non-linearity in the effect of alternatives’ attributes and to model individual heterogeneity. In this work, we present an alternative modeling approach based on non-parametric machine learning (ML) that is able to automatically segment the travelers and to take into account non-linear relationships within attributes of alternatives and characteristics of the decision maker. We test the models on a dataset consisting of flight searches and bookings on European markets. The experiments show our approach outperforming the standard and the latent class Multinomial Logit model in terms of accuracy and computation time, with less modeling effort.

Overview

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Crossref · 10.1016/j.jocm.2018.02.002

Cites

25 of 25 references verified0 citation errors · 0 not checkable

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Citations

Cites

Cited by

  • Status quo and challenges in air transport management research

    Wandelt, S., Antoniou, C., Birolini, S., Delahaye, D., Dresner, M., Fu, X., Gössling, S., Hong, S.-J., Odoni, A. R., Zanin, M., et al. (2024) · Journal of the Air Transport Research Society · doi:10.1016/j.jatrs.2024.100014 · Cited by 67

    “Therefore, researchers have developed various other models as well, e.g., integrating machine learning ( Lhéritier et al., 2019; Mottini and Acuna-Agost, 2017 ) and big data ( Mottini et al., 2018 ).”

  • Untitled work

    (2018) · ACM Conference on Recommender Systems (RecSys)

See all 114

Metadata

Authors
Lhéritier, A., Bocamazo, M., Delahaye, T., & Acuna-Agost, R.
Corresponding author
Lhéritier, Alix
ORCID
Lhéritier, Alix
Venue
Journal of Choice Modelling
Publisher
Elsevier BV
Published
2019-06
Volume
31
Pages
198-209
ISSN
1755-5345 (ISSN-L)
DOI
10.1016/j.jocm.2018.02.002
OpenAlex
W2616943032
Cited by
114
References
25
Citations by year
2026: 10 · 2025: 13 · 2024: 21 · 2023: 17 · 2022: 8 · 2021: 23 · 2020: 14 · 2019: 5
FWCI
17.13
Type
journal article
Language
en
Open access
closed
Licence
  • www.elsevier.com/tdm/userlicense/1.0/ (tdm)
  • www.elsevier.com/legal/tdmrep-license (tdm)
Locations
  • Journal of Choice Modelling (publishedVersion)
  • RePEc: Research Papers in Economics (submittedVersion)
Topics
  • Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Economic and Environmental Valuation
  • Social Sciences › Economics, Econometrics and Finance › General Economics, Econometrics and Finance › Aviation Industry Analysis and Trends
  • Social Sciences › Transportation › Transportation Planning and Optimization
Keywords
airline reservations, multinomial logistic regression, individual heterogeneity, travel demand

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.