Airline itinerary choice modeling using machine learning

Citations

Cited by:

1. Lu, Jing & Meng, Yucan & Timmermans, Harry & Zhang, Anming, 2021. "Modeling hesitancy in airport choice: A comparison of discrete choice and machine learning methods," Transportation Research Part A: Policy and Practice, Elsevier, vol. 147(C), pages 230-250.
2. Zhang, Xiaojian & Zhou, Zhengze & Xu, Yiming & Zhao, Xilei, 2024. "Analyzing spatial heterogeneity of ridesourcing usage determinants using explainable machine learning," Journal of Transport Geography, Elsevier, vol. 114(C).
3. Rodrigo Acuna-Agost & Eoin Thomas & Alix Lhéritier, 2021. "Price elasticity estimation for deep learning-based choice models: an application to air itinerary choices," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 20(3), pages 213-226, June.
4. Ortelli, Nicola & Hillel, Tim & Pereira, Francisco C. & de Lapparent, Matthieu & Bierlaire, Michel, 2021. "Assisted specification of discrete choice models," Journal of choice modelling, Elsevier, vol. 39(C).
5. Smeele, Nicholas V.R. & Chorus, Caspar G. & Schermer, Maartje H.N. & de Bekker-Grob, Esther W., 2023. "Towards machine learning for moral choice analysis in health economics: A literature review and research agenda," Social Science & Medicine, Elsevier, vol. 326(C).
6. Exequiel Salazar & Patricio Salas & Patricio Sáez, 2026. "Deep SHAP explanations for predicted choice probabilities from learning multinomial logit model in modal choice application," Quality & Quantity: International Journal of Methodology, Springer, vol. 60(1), pages 887-899, February.
7. Cheng, Haotian & Ng'ombe, John N. & Choi, Yejun & Kalinda, Thomson H. & Zheng, Shi, 2025. "Understanding the drivers of smallholder dairy cooperative participation in developing countries: Evidence from rural Zambia," Agricultural Systems, Elsevier, vol. 224(C).
8. Mourad Boudia & Suraj Mohamed & Nicolas Bondoux & Thierry Delahaye, 2021. "Traveler centric airline offer design and optimization," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 20(6), pages 634-645, December.
9. Niousha Bagheri & Milad Ghasri & Michael Barlow, 2025. "RUM-NN: A Neural Network Model Compatible with Random Utility Maximisation for Discrete Choice Setups," Papers 2501.05221, arXiv.org.
10. Xu, Ningzhe & Pena-Bastidas, Javier & Yang, Chenxuan & Liu, Jun & Hockstad, Trayce & Jones, Steven, 2025. "Urban and regional Air Mobility (URAM) and relocation decisions in the United States: Insights from a machine learning-supported path analysis," Transport Policy, Elsevier, vol. 170(C), pages 92-109.
11. Easton K. Huch & Michael P. Keane, 2026. "Amortized Inference for Correlated Discrete Choice Models via Equivariant Neural Networks," NBER Working Papers 35037, National Bureau of Economic Research, Inc.
    • Easton Huch & Michael Keane, 2026. "Amortized Inference for Correlated Discrete Choice Models via Equivariant Neural Networks," Papers 2603.24705, arXiv.org, revised Apr 2026.
12. Ali, Azam & Kalatian, Arash & Choudhury, Charisma F., 2023. "Comparing and contrasting choice model and machine learning techniques in the context of vehicle ownership decisions," Transportation Research Part A: Policy and Practice, Elsevier, vol. 173(C).
13. Eitan Bachmat & Sveinung Erland & Florian Jaehn & Simone Neumann, 2023. "Air Passenger Preferences: An International Comparison Affects Boarding Theory," Operations Research, INFORMS, vol. 71(3), pages 798-820, May.
14. Xu, Ningzhe & Nie, Qifan & Liu, Jun & Jones, Steven, 2024. "Linking short- and long-term impacts of the COVID-19 pandemic on travel behavior and travel preferences in Alabama: A machine learning-supported path analysis," Transport Policy, Elsevier, vol. 151(C), pages 46-62.
15. Xu, Yiming & Yan, Xiang & Liu, Xinyu & Zhao, Xilei, 2021. "Identifying key factors associated with ridesplitting adoption rate and modeling their nonlinear relationships," Transportation Research Part A: Policy and Practice, Elsevier, vol. 144(C), pages 170-188.
16. Han, Yafei & Pereira, Francisco Camara & Ben-Akiva, Moshe & Zegras, Christopher, 2022. "A neural-embedded discrete choice model: Learning taste representation with strengthened interpretability," Transportation Research Part B: Methodological, Elsevier, vol. 163(C), pages 166-186.
17. Lixun Liu & Yujiang Wang & Robin Hickman, 2023. "How Rail Transit Makes a Difference in People’s Multimodal Travel Behaviours: An Analysis with the XGBoost Method," Land, MDPI, vol. 12(3), pages 1-23, March.
18. Artemisa Zaragoza-Ibarra & Gerardo G. Alfaro-Calderón & Víctor G. Alfaro-García & Fernando Ornelas-Tellez & Rodrigo Gómez-Monge, 2021. "A machine learning model of national competitiveness with regional statistics of public expenditure," Computational and Mathematical Organization Theory, Springer, vol. 27(4), pages 451-468, December.
19. Morlotti, Chiara & Birolini, Sebastian & Malighetti, Paolo & Redondi, Renato, 2023. "A latent class approach to estimate air travelers’ propensity toward connecting itineraries," Research in Transportation Economics, Elsevier, vol. 99(C).
20. Xu, Yifan & Adler, Nicole & Wandelt, Sebastian & Sun, Xiaoqian, 2024. "Competitive integrated airline schedule design and fleet assignment," European Journal of Operational Research, Elsevier, vol. 314(1), pages 32-50.
21. Abdelghany, Ahmed & Guzhva, Vitaly S., 2022. "Exploratory analysis of air travel demand stimulation in first-time served markets," Journal of Air Transport Management, Elsevier, vol. 98(C).
22. S. Van Cranenburgh & S. Wang & A. Vij & F. Pereira & J. Walker, 2021. "Choice modelling in the age of machine learning -- discussion paper," Papers 2101.11948, arXiv.org, revised Nov 2021.
23. Sebastian Birolini & Alexandre Jacquillat & Phillip Schmedeman & Nuno Ribeiro, 2023. "Passenger-Centric Slot Allocation at Schedule-Coordinated Airports," Transportation Science, INFORMS, vol. 57(1), pages 4-26, January.
24. Yufeng Cao & Anton J. Kleywegt & He Wang, 2022. "Network Revenue Management Under a Spiked Multinomial Logit Choice Model," Operations Research, INFORMS, vol. 70(4), pages 2237-2253, July.
25. Youssef M. Aboutaleb & Mazen Danaf & Yifei Xie & Moshe Ben-Akiva, 2021. "Discrete Choice Analysis with Machine Learning Capabilities," Papers 2101.10261, arXiv.org.
26. Bagheri, Niousha & Ghasri, Milad & Barlow, Michael, 2025. "A neural estimation framework for discrete choice models with arbitrary error distributions," Journal of choice modelling, Elsevier, vol. 57(C).