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Passenger demand forecasting in scheduled transportation

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  • Banerjee, Nilabhra
  • Morton, Alec
  • Akartunalı, Kerem

Abstract

The aim of this review article is to provide a synoptic and critical evaluation of the extensive research that has been performed in demand forecasting in the scheduled passenger transportation industry, specifically in the last few decades. The review begins with an attempt to classify and tabulate the research according to the properties of proposed models, their objectives and application areas in industry in different stages of the planning cycle. This is followed by an assessment of forecast methodologies with suggestions on different methodologies that industry practitioners can adopt to suit their specific needs and recommendations towards future directions of research. We also provide a look into the cross cutting concerns that need to be addressed by all forecasting systems irrespective of the domain or planning stage, such as demand unconstraining, aggregation and the role of expert judgement to incorporate the effect of other extraneous factors that might affect the demand. We conclude from our study that there is a lack of standardization in the way in which methods are described and tested. As a result, there is a lack of cumulative knowledge building. To redress this concern, we propose open source testbeds to facilitate benchmarking of new models. We also propose a checklist as a guideline to standardize the research reports and suggest that when proposing newer models, researchers may consider including a comparative study with existing standard models in research report.

Suggested Citation

  • Banerjee, Nilabhra & Morton, Alec & Akartunalı, Kerem, 2020. "Passenger demand forecasting in scheduled transportation," European Journal of Operational Research, Elsevier, vol. 286(3), pages 797-810.
  • Handle: RePEc:eee:ejores:v:286:y:2020:i:3:p:797-810
    DOI: 10.1016/j.ejor.2019.10.032
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    1. Hsu, Chaug-Ing & Wen, Yuh-Horng, 2000. "Application of Grey theory and multiobjective programming towards airline network design," European Journal of Operational Research, Elsevier, vol. 127(1), pages 44-68, November.
    2. Fildes, Robert & Wei, Yingqi & Ismail, Suzilah, 2011. "Evaluating the forecasting performance of econometric models of air passenger traffic flows using multiple error measures," International Journal of Forecasting, Elsevier, vol. 27(3), pages 902-922, July.
    3. Silvia Riedel & Bogdan Gabrys, 2005. "Hierarchical Multilevel Approaches of Forecast Combination," Operations Research Proceedings, in: Hein Fleuren & Dick Hertog & Peter Kort (ed.), Operations Research Proceedings 2004, pages 479-486, Springer.
    4. Siem Jan Koopman & John A. D. Aston, 2006. "A non-Gaussian generalization of the Airline model for robust seasonal adjustment," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 25(5), pages 325-349.
    5. Bert Van Wee & David Banister, 2016. "How to Write a Literature Review Paper?," Transport Reviews, Taylor & Francis Journals, vol. 36(2), pages 278-288, March.
    6. Carson, Richard T. & Cenesizoglu, Tolga & Parker, Roger, 2011. "Forecasting (aggregate) demand for US commercial air travel," International Journal of Forecasting, Elsevier, vol. 27(3), pages 923-941, July.
    7. Peng Guo & Baichun Xiao & Jun Li, 2012. "Unconstraining Methods in Revenue Management Systems: Research Overview and Prospects," Advances in Operations Research, Hindawi, vol. 2012, pages 1-23, July.
    8. Rongfang Liu & Andy Li, 2012. "Forecasting high-speed rail ridership using a simultaneous modeling approach," Transportation Planning and Technology, Taylor & Francis Journals, vol. 35(5), pages 577-590, June.
    9. de Menezes, Lilian M. & W. Bunn, Derek & Taylor, James W., 2000. "Review of guidelines for the use of combined forecasts," European Journal of Operational Research, Elsevier, vol. 120(1), pages 190-204, January.
    10. Saab, Samer S. & Zouein, Pierrette P., 2001. "Forecasting passenger load for a fixed planning horizon," Journal of Air Transport Management, Elsevier, vol. 7(6), pages 361-372.
    11. Kalyan Talluri & Garrett van Ryzin, 2004. "Revenue Management Under a General Discrete Choice Model of Consumer Behavior," Management Science, INFORMS, vol. 50(1), pages 15-33, January.
    12. Lewe, J.-H. & Hivin, L.F. & Mavris, D.N., 2014. "A multi-paradigm approach to system dynamics modeling of intercity transportation," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 71(C), pages 188-202.
    13. Samagaio, António & Wolters, Mark, 2010. "Comparative analysis of government forecasts for the Lisbon Airport," Journal of Air Transport Management, Elsevier, vol. 16(4), pages 213-217.
    14. Jeffrey I. McGill & Garrett J. van Ryzin, 1999. "Revenue Management: Research Overview and Prospects," Transportation Science, INFORMS, vol. 33(2), pages 233-256, May.
    15. Boylan, John E. & Goodwin, Paul & Mohammadipour, Maryam & Syntetos, Aris A., 2015. "Reproducibility in forecasting research," International Journal of Forecasting, Elsevier, vol. 31(1), pages 79-90.
    16. William L. Cooper & Tito Homem-de-Mello & Anton J. Kleywegt, 2006. "Models of the Spiral-Down Effect in Revenue Management," Operations Research, INFORMS, vol. 54(5), pages 968-987, October.
    17. Hyndman, Rob J. & Koehler, Anne B., 2006. "Another look at measures of forecast accuracy," International Journal of Forecasting, Elsevier, vol. 22(4), pages 679-688.
    18. Hannah Kollwitz & Alexis Papathanassis, 2011. "Evaluating Cruise Demand Forecasting Practices: A Delphi Approach," Springer Books, in: Philip Gibson & Alexis Papathanassis & Petra Milde (ed.), Cruise Sector Challenges, chapter 3, pages 39-55, Springer.
    19. Nataša Glišović & Miloš Milenković & Nebojša Bojović & Libor Švadlenka & Zoran Avramović, 2016. "A hybrid model for forecasting the volume of passenger flows on Serbian railways," Operational Research, Springer, vol. 16(2), pages 271-285, July.
    20. Dupuis, Christine & Gamache, Michel & Pagé, Jean-François, 2012. "Logical analysis of data for estimating passenger show rates at Air Canada," Journal of Air Transport Management, Elsevier, vol. 18(1), pages 78-81.
    21. R Fildes & K Nikolopoulos & S F Crone & A A Syntetos, 2008. "Forecasting and operational research: a review," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 59(9), pages 1150-1172, September.
    22. Romero Morales, Dolores & Wang, Jingbo, 2010. "Forecasting cancellation rates for services booking revenue management using data mining," European Journal of Operational Research, Elsevier, vol. 202(2), pages 554-562, April.
    23. Theodore Tsekeris & Charalambos Tsekeris, 2011. "Demand Forecasting in Transport: Overview and Modeling Advances," Economic Research-Ekonomska Istraživanja, Taylor & Francis Journals, vol. 24(1), pages 82-94, January.
    24. Goutam Dutta & Divya Pachisia Marodia, 2015. "Comparison of forecasting techniques in revenue management for a national railway in an emerging Asian economy," International Journal of Revenue Management, Inderscience Enterprises Ltd, vol. 8(2), pages 130-152.
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