IDEAS home Printed from https://ideas.repec.org/a/wly/jforec/v43y2024i3p661-672.html
   My bibliography  Save this article

Forecasting air passenger travel: A case study of Norwegian aviation industry

Author

Listed:
  • Angesh Anupam
  • Isah A. Lawal

Abstract

Accurate forecasting of airline passenger traffic is important for facilitating the effective management and planning of aviation resources. In this study, we explore the air passenger traffic in the Norwegian aviation industry by collecting the passenger flow data and the corresponding measurements of the weather conditions affecting the flow from the different airports in Norway. We then proposed nonlinear autoregressive with exogenous input (NARX) forecasting models to predict air passenger traffic in advance. The NARX models account for the nonlinearity and nonstationarity in the passenger flow and allow the accurate forecasting of air passenger traffic. We perform experiments to demonstrate the effectiveness of two variants of the NARX model and compare their performances against long short‐term memory (LSTM), a deep learning method. We show that the proposed NARX model achieves the best prediction accuracy compared to LSTM, which is considered as a state‐of‐the‐art approach for fitting sequential data.

Suggested Citation

  • Angesh Anupam & Isah A. Lawal, 2024. "Forecasting air passenger travel: A case study of Norwegian aviation industry," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(3), pages 661-672, April.
    • Handle: RePEc:wly:jforec:v:43:y:2024:i:3:p:661-672
    DOI: 10.1002/for.3051
    as

    Download full text from publisher

    File URL: https://doi.org/10.1002/for.3051
    Download Restriction: no
    File URL: https://libkey.io/10.1002/for.3051?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Tsui, Wai Hong Kan & Ozer Balli, Hatice & Gilbey, Andrew & Gow, Hamish, 2014. "Forecasting of Hong Kong airport's passenger throughput," Tourism Management, Elsevier, vol. 42(C), pages 62-76.
    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. Mao, Liang & Wu, Xiao & Huang, Zhuojie & Tatem, Andrew J., 2015. "Modeling monthly flows of global air travel passengers: An open-access data resource," Journal of Transport Geography, Elsevier, vol. 48(C), pages 52-60.
    4. Elena Olmedo, 2016. "Comparison of Near Neighbour and Neural Network in Travel Forecasting," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 35(3), pages 217-223, April.
    5. 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.
    6. Juan R. Trapero & Robert Fildes & Andrey Davydenko, 2011. "Nonlinear identification of judgmental forecasts effects at SKU level," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 30(5), pages 490-508, August.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Weifan Gu & Baohua Guo & Zhezhe Zhang & He Lu, 2024. "Civil Aviation Passenger Traffic Forecasting: Application and Comparative Study of the Seasonal Autoregressive Integrated Moving Average Model and Backpropagation Neural Network," Sustainability, MDPI, vol. 16(10), pages 1-17, May.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Wang, Sen & Gao, Yi, 2021. "A literature review and citation analyses of air travel demand studies published between 2010 and 2020," Journal of Air Transport Management, Elsevier, vol. 97(C).
    2. Gunter, Ulrich & Zekan, Bozana, 2021. "Forecasting air passenger numbers with a GVAR model," Annals of Tourism Research, Elsevier, vol. 89(C).
    3. Song, Haiyan & Qiu, Richard T.R. & Park, Jinah, 2019. "A review of research on tourism demand forecasting," Annals of Tourism Research, Elsevier, vol. 75(C), pages 338-362.
    4. 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.
    5. Chen, Jieh-Haur & Wei, Hsi-Hsien & Chen, Chih-Lin & Wei, Hsin-Yi & Chen, Yi-Ping & Ye, Zhongnan, 2020. "A practical approach to determining critical macroeconomic factors in air-traffic volume based on K-means clustering and decision-tree classification," Journal of Air Transport Management, Elsevier, vol. 82(C).
    6. Rafael Bernardo Carmona-Benítez & María Rosa Nieto, 2017. "Comparison of bootstrap estimation intervals to forecast arithmetic mean and median air passenger demand," Journal of Applied Statistics, Taylor & Francis Journals, vol. 44(7), pages 1211-1224, May.
    7. Sun, Shaolong & Lu, Hongxu & Tsui, Kwok-Leung & Wang, Shouyang, 2019. "Nonlinear vector auto-regression neural network for forecasting air passenger flow," Journal of Air Transport Management, Elsevier, vol. 78(C), pages 54-62.
    8. Dobruszkes, Frédéric & Vandermotten, Christian, 2022. "Do scale and the type of markets matter? Revisiting the determinants of passenger air services worldwide," Journal of Air Transport Management, Elsevier, vol. 99(C).
    9. Jiao, Xiaoying & Chen, Jason Li & Li, Gang, 2021. "Forecasting tourism demand: Developing a general nesting spatiotemporal model," Annals of Tourism Research, Elsevier, vol. 90(C).
    10. Aydın, Umut & Ülengin, Burç, 2022. "Analyzing air cargo flows of Turkish domestic routes: A comparative analysis of gravity models," Journal of Air Transport Management, Elsevier, vol. 102(C).
    11. Hu, Yi-Chung, 2023. "Air passenger flow forecasting using nonadditive forecast combination with grey prediction," Journal of Air Transport Management, Elsevier, vol. 112(C).
    12. Xu, Shuojiang & Chan, Hing Kai & Zhang, Tiantian, 2019. "Forecasting the demand of the aviation industry using hybrid time series SARIMA-SVR approach," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 122(C), pages 169-180.
    13. Plakandaras, Vasilios & Papadimitriou, Theophilos & Gogas, Periklis, 2019. "Forecasting transportation demand for the U.S. market," Transportation Research Part A: Policy and Practice, Elsevier, vol. 126(C), pages 195-214.
    14. Frédéric Dobruszkes & Christian Vandermotten, 2022. "Do scale and the type of markets matter? Revisiting the determinants of passenger air services worldwide," ULB Institutional Repository 2013/336304, ULB -- Universite Libre de Bruxelles.
    15. Tillmann, Andreas M. & Joormann, Imke & Ammann, Sabrina C.L., 2023. "Reproducible air passenger demand estimation," Journal of Air Transport Management, Elsevier, vol. 112(C).
    16. Shaolong Suna & Dan Bi & Ju-e Guo & Shouyang Wang, 2020. "Seasonal and Trend Forecasting of Tourist Arrivals: An Adaptive Multiscale Ensemble Learning Approach," Papers 2002.08021, arXiv.org, revised Mar 2020.
    17. Azarnoosh Kafi & Behrouz Daneshian & Mohsen Rostamy-Malkhalifeh, 2021. "Forecasting the confidence interval of efficiency in fuzzy DEA," Operations Research and Decisions, Wroclaw University of Science and Technology, Faculty of Management, vol. 31(1), pages 41-59.
    18. Armstrong, J. Scott & Green, Kesten C. & Graefe, Andreas, 2015. "Golden rule of forecasting: Be conservative," Journal of Business Research, Elsevier, vol. 68(8), pages 1717-1731.
    19. Franses, Philip Hans & Legerstee, Rianne, 2013. "Do statistical forecasting models for SKU-level data benefit from including past expert knowledge?," International Journal of Forecasting, Elsevier, vol. 29(1), pages 80-87.
    20. Fullerton, Thomas M., Jr. & Mukhopadhyay, Somnath, 2013. "Border Region Bridge and Air Transport Predictability," MPRA Paper 59583, University Library of Munich, Germany, revised 11 Jul 2013.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:wly:jforec:v:43:y:2024:i:3:p:661-672. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Wiley Content Delivery (email available below). General contact details of provider: http://www3.interscience.wiley.com/cgi-bin/jhome/2966 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.