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

Author

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  • 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
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    Cited by:

    1. 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).
    2. 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).
    3. 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).
    4. 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.
    5. 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.
    6. 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).
    7. Gunter, Ulrich & Zekan, Bozana, 2021. "Forecasting air passenger numbers with a GVAR model," Annals of Tourism Research, Elsevier, vol. 89(C).
    8. 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.

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