IDEAS home Printed from https://ideas.repec.org/a/taf/transp/v35y2012i3p373-392.html
   My bibliography  Save this article

Improving the forecasting accuracy of air passenger and air cargo demand: the application of back-propagation neural networks

Author

Listed:
  • Shu-Chuan Chen
  • Shih-Yao Kuo
  • Kuo-Wei Chang
  • Yi-Ting Wang

Abstract

This study employs back-propagation neural networks (BPN) to improve the forecasting accuracy of air passenger and air cargo demand from Japan to Taiwan. The factors which influence air passenger and air cargo demand are identified, evaluated and analysed in detail. The results reveal that some factors influence both passenger and cargo demand, and the others only one of them. The forecasting accuracy of air passenger and air cargo demand has been improved efficiently by the proposed procedure to evaluate input variables. The established model improves dramatically the forecasting accuracy of air passenger demand with an extremely low mean absolute percentage error (MAPE) of 0.34% and 7.74% for air cargo demand.

Suggested Citation

  • Shu-Chuan Chen & Shih-Yao Kuo & Kuo-Wei Chang & Yi-Ting Wang, 2012. "Improving the forecasting accuracy of air passenger and air cargo demand: the application of back-propagation neural networks," Transportation Planning and Technology, Taylor & Francis Journals, vol. 35(3), pages 373-392, April.
    • Handle: RePEc:taf:transp:v:35:y:2012:i:3:p:373-392
    DOI: 10.1080/03081060.2012.673272
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/03081060.2012.673272
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/03081060.2012.673272?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

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


    Cited by:

    1. Ari, Didem & Mizrak Ozfirat, Pinar, 2024. "Comparison of artificial neural networks and regression analysis for airway passenger estimation," Journal of Air Transport Management, Elsevier, vol. 115(C).
    2. Güner, Samet & Cebeci, Halil İbrahim, 2021. "Output targeting and capacity utilization for a new-built airport: Analysis for the new airport in Istanbul," Socio-Economic Planning Sciences, Elsevier, vol. 76(C).
    3. Wang, Lu & Ruan, Hang & Hong, Yanran & Luo, Keyu, 2023. "Detecting the hidden asymmetric relationship between crude oil and the US dollar: A novel neural Granger causality method," Research in International Business and Finance, Elsevier, vol. 64(C).
    4. Li Long, Chan & Guleria, Yash & Alam, Sameer, 2021. "Air passenger forecasting using Neural Granger causal Google trend queries," Journal of Air Transport Management, Elsevier, vol. 95(C).
    5. Meena Madhavan & Mohammed Ali Sharafuddin & Pairach Piboonrungroj & Ching-Chiao Yang, 2023. "Short-term Forecasting for Airline Industry: The Case of Indian Air Passenger and Air Cargo," Global Business Review, International Management Institute, vol. 24(6), pages 1145-1179, December.
    6. Binglei Xie & Yu Sun & Xiaolong Huang & Le Yu & Gangyan Xu, 2020. "Travel Characteristics Analysis and Passenger Flow Prediction of Intercity Shuttles in the Pearl River Delta on Holidays," Sustainability, MDPI, vol. 12(18), pages 1-23, September.
    7. 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).
    8. Gunter, Ulrich & Zekan, Bozana, 2021. "Forecasting air passenger numbers with a GVAR model," Annals of Tourism Research, Elsevier, vol. 89(C).
    9. Fabian Baier & Peter Berster & Marc Gelhausen, 2022. "Global cargo gravitation model: airports matter for forecasts," International Economics and Economic Policy, Springer, vol. 19(1), pages 219-238, February.

    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:taf:transp:v:35:y:2012:i:3:p:373-392. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/GTPT20 .

    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.