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Bootstrap Estimation Intervals Using Bias Corrected Accelerated Method to Forecast Air Passenger Demand

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Computational Logistics (ICCL 2015)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9335))

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Abstract

The aim of this paper is to propose an approach for forecasting passenger (pax) demand between airports based on the median pax demand and distance. The approach is based on three phases. First, the implement of bootstrap procedures to estimate the distribution of the mean pax demand and the median pax demand for each block of routes distance; second, the estimate pax demand by calculating boostrap confidence intervals for the mean pax demand and the median pax demand using bias corrected accelerated method (BCa); and third, by carrying out Monte Carlo experiments to analyse the finite sample performance of the proposed bootstrap procedure. The results indicate that in the air transport industry it is important to estimate the median of the pax demand.

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References

  1. Alekseev, K.P.G., Seixas, J.M.: Forecasting the air transport demand for passengers with Neural Modelling. In: The VII Brazilian Symposium on Neural Networks IEEE Computer Society Washington, Recife, Brazil, pp. 86–91 (2002)

    Google Scholar 

  2. Beckmann, M.J., Bobkoski, F.: Airline demand: An analysis of some frequency distributions. Naval research logistics quarterly 5(1), 43–51 (1958)

    Article  Google Scholar 

  3. Carmona-Benitez, R.B.: The design of a large scale airline network (PhD Dissertation). Delft University of Technology, The Netherlands: TRAIL Research School, pp. 81–106 (2012)

    Google Scholar 

  4. Carmona-Benitez, R.B., Carmona-Paredes, R.B., Lodewijks, G., Nabais, J.L.: Damp trend Grey Model forecasting method for airline industry. Expert System with Applications 40(12), 4915–4921 (2013)

    Article  Google Scholar 

  5. Coldren, G.M., Koppelman, F.S., Kasturirangan, K., Mukherjee, A.: Modelling aggregate air travel itinerary shares: logit model development at a major US airline. Journal of Air Transport Management 9(6), 841–851 (2003)

    Article  Google Scholar 

  6. DOT US Department of Transportation: Domestic Airline Fare Consumer Report (2005–2008). http://ostpxweb.dot.gov/aviation/X-50%20Role_files/airportcompdefinition.htm (cited 26/02/2011)

  7. Efron, B.: Bootstrap methods: another look at the jackknife. Annals of Statistics 7, 1–26 (1979)

    Article  MathSciNet  MATH  Google Scholar 

  8. Efron, B.: Better bootstrap with dense intervals (with discussion). Journal of the American Statistical Association 82, 171–200 (1987)

    Article  MathSciNet  MATH  Google Scholar 

  9. Grosche, T., Rothlauf, F., Heinzl, A.: Gravity models for airline passenger volume estimation. Journal of Air Transport Management 13, 175–183 (2007)

    Article  Google Scholar 

  10. Hsu, C.I., Wen, Y.H.: Application of Grey theory and multi objective programming towards airline network design. European journal of operation research 127(1), 44–68 (2000)

    Article  MATH  Google Scholar 

  11. Hsu, C.I., Wen, Y.H.: Reliability evolution for airline network design in response to fluctuation in passenger demand. Omega – The International Journal of Management Science 30(3), 197–213 (2002)

    Article  Google Scholar 

  12. IATA: Successful Vision 2050 Meeting Concludes – Building a Sustainable Future (2011b). http://www.iata.org/pressroom/pr/pages/2011-02-14-01.aspx (cited 22/08/2011)

  13. Quenouille, M.H.: Problems in Plane Sampling. The Annals of Mathematical Statistics 20(3), 355–375 (1949)

    Article  MathSciNet  MATH  Google Scholar 

  14. Quenouille, M.H.: Notes on Bias in Estimation. Biometrika 43(3–4), 353–360 (1956)

    Article  MathSciNet  MATH  Google Scholar 

  15. Shaw, R.: Forecasting air traffic: are there limits to growth? Futures 11(3), 185–194 (1979)

    Article  Google Scholar 

  16. Shen, G.: Reverse-fitting the gravity model to inter-city airline passenger flows by an algebraic simplification. Journal of Transport Geography 16(4), 213–217 (2004)

    Google Scholar 

  17. Swan, M.: A System Analysis of Schedule Air Transportation Networks. Report FTL-R79-5. MIT, Cambridge (1979)

    Google Scholar 

  18. Wei, Z., Jinfu, Z.: Passenger traffic forecast based on the Grey-Markov method. In: IEEE International Conference on Grey Systems, pp. 630–633 (2009)

    Google Scholar 

  19. Zeni, R.H.: Improved forecast accuracy in revenue management by unconstraining demand estimates from censored data, in: Dissertation.com, PhD in management program, New Jersey, United States of America (2001)

    Google Scholar 

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Author information

Authors and Affiliations

  1. School of Business and Economics, Universidad Anáhuac México Norte, Av. Universidad Anáhuac no. 46, Col. Lomas Anáhuac, 52786, Huixquilucan, State of Mexico, Mexico

    Rafael Bernardo Carmona-Benítez & María Rosa Nieto-Delfín

Authors
  1. Rafael Bernardo Carmona-Benítez
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  2. María Rosa Nieto-Delfín
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Corresponding author

Correspondence to Rafael Bernardo Carmona-Benítez .

Editor information

Editors and Affiliations

  1. Delft University of Technology, Delft, The Netherlands

    Francesco Corman

  2. University of Hamburg, Hamburg, Germany

    Stefan Voß

  3. Delft University of Technology, Delft, The Netherlands

    Rudy R. Negenborn

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Carmona-Benítez, R.B., Nieto-Delfín, M.R. (2015). Bootstrap Estimation Intervals Using Bias Corrected Accelerated Method to Forecast Air Passenger Demand. In: Corman, F., Voß, S., Negenborn, R. (eds) Computational Logistics. ICCL 2015. Lecture Notes in Computer Science(), vol 9335. Springer, Cham. https://doi.org/10.1007/978-3-319-24264-4_22

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  • DOI: https://doi.org/10.1007/978-3-319-24264-4_22

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-24263-7

  • Online ISBN: 978-3-319-24264-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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