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Cited by 7Aviation Industry Analysis and TrendsAir Traffic Management and OptimizationTransportation Planning and Optimization

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Behavioral Models for the Estimation of the Air Transport Demand: The Case Study of Rome-London Flight Connection

Open access
Cipriani, Ernesto, Crescenzi, Luca, Nigro, Marialisa·2014·Procedia - Social and Behavioral Sciences
DOI 10.1016/j.sbspro.2014.01.040
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Abstract

Obtaining reliable estimates of the air transport demand is fundamental for both the short and the long term: namely, an underestimation or an overestimation of demand leads to an inefficient use of human, management and economic resources. The key players in the air transport system are airline companies and airport operators: for the airline companies, the forecasts of the demand level allows to define in the short-term the supply characteristics, while in the long term allow to plan investments consistent with the characteristics of the expected demand. On the other hand, airport operators need to forecast the levels of demand in the short term in order to define the design and operation of the spaces, while in the long term to plan investments for both land-side and air-side. The paper develops different utility models (Multinomial Logit MNL, Nested Logit NL, Cross-Nested Logit) in order to describe the choice of the users about the flight. The models have been calibrated using different data sets for the real case study of the flight connection between the city of Rome (Italy) and the city of London (England). Results demonstrated the capability of the models to be a reliable decision support system for both the airline companies and the airport operators.

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Research works printed but not linked: 9

  1. Bonvino, E., Ottomanelli, M., & Sassanelli, D. (2009). Sketch Models for Air Transport Demand Estimation. Transportation Research Record: Journal of the Transportation Research Board, No. 2106, Transportation Research Board of the National Academies, Washington, D.C., 2009, pp. 1-11. · DOI
  2. Janic, M. (2004).Expansion of Airport Capacity at London Heathrow Airport. Transportation Research Record: Journal of the Transportation Research Board, No. 1888, TRB, National Research Council, Washington, D.C., 2004, pp. 7-14. · DOI
  3. De Luca, S. (2008). Analysis of competition between airports: travellers airport choice models. In Marino Lupi Methods and models for planning the development of regional airport systems. pp.59-73 MILANO, Franco Angeli. ISBN: 9788856803273.
  4. Rezaei, A., Puckett, S.M., & Nassiri, H. (2011). Heterogeneity in Preferences of Air Travel Itinerary in a Low-Frequency Market. Transportation Research Record: Journal of the Transportation Research Board, No. 2214, Transportation Research Board of the National Academies, Washington, D.C., 2011, pp. 10-19. · DOI
  5. Warburg, V., Bhat, C., & Adler, T. (2006). Modeling Demographic and Unobserved Heterogeneity in Air Passengers’ Sensitivity to Service Attributes in Itinerary Choice. Transportation Research Record: Journal of the Transportation Research Board, No. 1951, Transportation Research Board of the National Academies, Washington, D.C., 2006, pp. 7-16. · DOI
  6. Luken, B.L., & Garrow L.A. (2011). Multiairport Choice Models for the New York Metropolitan Area Application Based on Ticketing Data. Transportation Research Record: Journal of the Transportation Research Board, No. 2206, Transportation Research Board of the National Academies, Washington, D.C., 2011, pp. 24-31. · DOI
  7. Hess, S., & Polak. J.W. (2005). Accounting for Random Taste Heterogeneity in Airport Choice Modeling. Transportation Research Record: Journal of the Transportation Research Board, No. 1915, Transportation Research Board of the National Academies, Washington, D.C., 2005, pp. 36-43. · DOI
  8. Festa, D.C., Condino, D., & Mazzulla G. (2006). Experimental tour-based travel demand models. European Journal of Operational Research. [175] (2006) 1472-1483. · DOI
  9. Cascetta, E. (2008). Transportation Systems Analysis: Models and Applications. Springer-verlag New York Inc. 2008.

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Metadata

Authors
Cipriani, E., Crescenzi, L., & Nigro, M.
Affiliations
Roma Tre University (IT)
Corresponding author
Nigro, Marialisa
ORCID
Venue
Procedia - Social and Behavioral Sciences
Publisher
Elsevier BV
Published
2014-02
Volume
111
Pages
78-87
ISSN
1877-0428 (ISSN-L)
DOI
10.1016/j.sbspro.2014.01.040
OpenAlex
W2075278616
Cited by
7
References
0
Citations by year
2025: 1 · 2024: 1 · 2020: 1 · 2019: 2 · 2016: 1 · 2015: 1
FWCI
7.30
Type
conference paper
Language
en
Open access
Licence
Locations
  • Procedia - Social and Behavioral Sciences (publishedVersion, cc-by-nc-nd)
  • Iris (Roma Tre University) (submittedVersion)
Topics
  • Social Sciences › Economics, Econometrics and Finance › General Economics, Econometrics and Finance › Aviation Industry Analysis and Trends
  • Physical Sciences › Engineering › Aerospace Engineering › Air Traffic Management and Optimization
  • Social Sciences › Transportation › Transportation Planning and Optimization
Keywords
multinomial logistic regression, nested logit model, cross-nested logit

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CoChat. (2026). Citation check: Behavioral Models for the Estimation of the Air Transport Demand: The Case Study of Rome-London Flight Connection. https://app.cochat.ai/papers/10.1016/j.sbspro.2014.01.040
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Cipriani, E., Crescenzi, L., & Nigro, M. (2014). Behavioral Models for the Estimation of the Air Transport Demand: The Case Study of Rome-London Flight Connection. Procedia - Social and Behavioral Sciences. https://doi.org/10.1016/j.sbspro.2014.01.040

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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.