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A market spill–recapture unconstraining model for estimating airline true demand

  • Tomasz Drabas
  • Cheng-Lung Wu
Research Article
  • 1 Downloads

Abstract

The true demand of airline tickets is defined as the number of passengers that would book a particular flight if there was no capacity constraint. Estimating the true demand plays a crucial role in airline revenue management and pricing. In this paper, we developed a true demand unconstraining model that incorporated passenger choice models, allowing us to simultaneously estimate true demand for all booking classes in a market. Simulated booking data were generated to inform the true demand that is unavailable in the real-world setting. Numerical experiments showed that our model could outperform a benchmarking model by up to 10%.

Keywords

Demand unconstraining model True demand Demand spill Demand recapture Cross-nested logit model Multinomial logit model 

Notes

References

  1. Al-Sayer, F.A. (2001) MCMC Simulation for Modelling Airline Passenger Choice Behaviour. Master’s thesis, School of Computer Applications, Dublin City University.Google Scholar
  2. Andersson, S.E. 1998. Passenger Choice Analysis for Seat Capacity Control: A Pilot Project in Scandinavian Airlines. International Transactions in Operational Research 5 (6): 471–486.CrossRefGoogle Scholar
  3. Barnhart, C., P.P. Belobaba, and A.R. Odoni. 2003. Applications of Operations Research in the Air Transport Industry. Transportation Science 37 (4): 368–391.CrossRefGoogle Scholar
  4. Beckman, J.M., and F. Bobkowski. 1958. Airline Demand: An Analysis of Some Frequency Distributions. Naval Research Logistics Quarterly 5: 43–51.CrossRefGoogle Scholar
  5. Belobaba, P.P. 1987. Air Travel Demand and Airline Seat Inventory. Ph.D. thesis, Massachusetts Institute of Technology, Department of Aeronautics and Astronautics.Google Scholar
  6. Ben Akiva, M., and S.R. Lerman. 1985. Discrete Choice Analysis: Theory and Application to Travel Demand. Cambridge: The MIT Press.Google Scholar
  7. Bierlaire, M. 2003. BIOGEME: A Free Package for the Estimation of Discrete Choice Models. In Proceedings of the 3rd Swiss Transportation Research Conference. Ascona, Switzerland.Google Scholar
  8. Bierlaire, M. 2006. A Theoretical Analysis of the Cross-Nested Logit Model. Annals of Operations Research 144 (1): 287–300.CrossRefGoogle Scholar
  9. Boyd, E.A., Kambour E., and Tama J. 2001. The Impact of Buy-Down on Sell Up, Unconstraining, and Spiral-Down. In Proceedings of the 1st Annual INFORMS Revenue Management Section Conference. New York: INFORMS.Google Scholar
  10. Drabas, T., and C.L. Wu. 2013. Modelling Air Carrier Choices With a Segment Specific Cross Nested Logit Model: An Example from Australian Market. Journal of Air Transport Management 32: 8–16.  https://doi.org/10.1016/j.jairtraman.2013.04.004.CrossRefGoogle Scholar
  11. Garrity, T.A. 2001. All the Mathematics You Missed But Need to Know for Graduate School. New York: Cambridge University Press.CrossRefGoogle Scholar
  12. Garrow, L. 2010. Discrete Choice Modelling and Air Travel Demand—Theory and Applications. Farnham: Ashgate Publishing Limited.Google Scholar
  13. Gorin, T. 2000. Airline Revenue Management: Sell-Up and Forecasting Algorithms. Master’s thesis, Massachusetts Institute of Technology.Google Scholar
  14. Hensher, D.A., J.M. Rose, and W.H. Greene. 2005. Applied Choice Analysis—A Primer. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
  15. Hopperstad, C. 1997. Projection Detruncation. Renton, WA: Boeing Company.Google Scholar
  16. Littlewood, K. 1972. Forecasting and Control of Passenger Bookings. In AGIFORS Proceedings.Google Scholar
  17. McFadden, D.L. 1974. Frontiers in Economics, chap. Conditional Logit Analysis of Qualitative Choice Behavior, 105–142. New York: Academic Press.Google Scholar
  18. Nikseresht A., and Ziarati K. 2015. Review on the Newest Revenue Management Demand Forecasting Methods. In Proceedings of the International Conference on Management, Economics and Industrial Engineering 1(1).Google Scholar
  19. Nikseresht, A., and K. Ziarati. 2017. Estimating True Demand in Airline’s Revenue Management Systems using Observed Sales. International Journal of Advanced Computer Science and Applications 8 (7): 361–369.CrossRefGoogle Scholar
  20. Papola, A. 2004. Some Developments on the Cross-Nested Logit Model. Transportation Research Part B: Methodological 38 (9): 833–851.CrossRefGoogle Scholar
  21. Queenan, C.C., M. Ferguson, J. Higbie, and R. Kapoor. 2007. A Comparison of Unconstraining Methods to Improve Revenue Management Systems. Production and Operations Management 16 (6): 729–746.CrossRefGoogle Scholar
  22. Ratliff, R.M., B.V. Rao, C.P. Narayan, and K. Yellepeddi. 2008. A Multi-Flight Recapture Heuristic for Estimating Unconstrained Demand from Airline Bookings. Journal of Revenue & Pricing Management 7 (2): 153–171.CrossRefGoogle Scholar
  23. Sharif, A.S., P. Marcotte, and G. Savard. 2014. A Taxonomy of Demand Uncensoring Methods. Journal of Revenue & Pricing Management.  https://doi.org/10.1057/rpm.2014.8.Google Scholar
  24. Train, K. 2009. Discrete Choice Methods with Simulation, 2nd ed. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
  25. van Ryzin, G., and G. Vulcano. 2014. A Market Discovery Algorithm to Estimate a General Class of Nonparametric Choice Models. Management Science 61 (2): 281–300.CrossRefGoogle Scholar
  26. van Ryzin, G., and G. Vulcano. 2017. An Expectation–Maximization Method to Estimate a Rank-Based Choice Model of Demand. Operations Research 65 (2): 396–407.CrossRefGoogle Scholar
  27. Vulcano, G., G.J. van Ryzin, and W. Chaar. 2010. Choice-Based Revenue Management: An Empirical Study of Estimation and Optimisation. Manufacturing & Service Operations Research 12 (3): 371–392.CrossRefGoogle Scholar
  28. Vulcano, G., G.J. van Ryzin, and R.M. Ratliff. 2012. Estimating Primary Demand for Substitutable Products from Sales Transaction Data. Operations Research 60 (2): 313–334.CrossRefGoogle Scholar
  29. Weatherford, L.R., and S. Pölt. 2002. Better Unconstraining of Airline Demand Data in Revenue Management Systems for Improved Forecast Accuracy. Journal of Revenue & Pricing Management 1 (3): 234–254.CrossRefGoogle Scholar
  30. Zeni, R.H. 2001. Improved Forecast Accuracy in Revenue Management by Unconstraining Demand Estimates from Censored Data. Ph.D. thesis, Graduate School-Newark Rutgers, The State University of New Jersey.Google Scholar
  31. Zickus, J.S. 1998. Forecasting for Airline Network Revenue Management: Revenue and Competitive Impacts. Master’s thesis, Massachusetts Institute of Technology, Department of Civil and Environmental Engineering.Google Scholar

Copyright information

© Springer Nature Limited 2019

Authors and Affiliations

  1. 1.School of AviationUNSW SydneySydneyAustralia

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