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Nonlinear vector auto-regression neural network for forecasting air passenger flow

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  • Sun, Shaolong
  • Lu, Hongxu
  • Tsui, Kwok-Leung
  • Wang, Shouyang

Abstract

Forecasting air passenger flows is receiving increasing attention, especially due to its intrinsic difficulties and wide applications. Total passengers are used as a proxy for air transport demand. However, the time series of air passenger flows usually has complicated behavior with high volatility and irregularity. This paper proposes a MIV-based nonlinear vector auto-regression neural network (NVARNN) approach to forecast air passenger flows. In the proposed MIV-NVARNN learning approach, (1) a method of mean impact value (MIV) based on neural network is used for identifying and extracting input variables; (2) NVARNN is firstly proposed to deal with the irregularity and volatility of the time series of air passenger flows. To illustrate and verify the effectiveness of the proposed approach, we tested its directional and level forecasting accuracy using the time series of Beijing International Airport's passenger flows. The results of out-of-sample forecasting performance show that the proposed MIV-NVARNN approach consistently outperforms single models and other hybrid approaches in terms of level forecasting accuracy, directional forecasting accuracy and robustness analysis.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:jaitra:v:78:y:2019:i:c:p:54-62
    DOI: 10.1016/j.jairtraman.2019.04.005
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    as
    1. Li, Zhi-Chun & Sheng, Dian, 2016. "Forecasting passenger travel demand for air and high-speed rail integration service: A case study of Beijing-Guangzhou corridor, China," Transportation Research Part A: Policy and Practice, Elsevier, vol. 94(C), pages 397-410.
    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. Yibing Wang & Markos Papageorgiou & Albert Messmer, 2007. "Real-Time Freeway Traffic State Estimation Based on Extended Kalman Filter: A Case Study," Transportation Science, INFORMS, vol. 41(2), pages 167-181, May.
    4. Medeiros, Marcelo C. & McAleer, Michael & Slottje, Daniel & Ramos, Vicente & Rey-Maquieira, Javier, 2008. "An alternative approach to estimating demand: Neural network regression with conditional volatility for high frequency air passenger arrivals," Journal of Econometrics, Elsevier, vol. 147(2), pages 372-383, December.
    5. Kim, Myung Suk, 2016. "Analysis of short-term forecasting for flight arrival time," Journal of Air Transport Management, Elsevier, vol. 52(C), pages 35-41.
    6. Zhang, Guoqiang & Eddy Patuwo, B. & Y. Hu, Michael, 1998. "Forecasting with artificial neural networks:: The state of the art," International Journal of Forecasting, Elsevier, vol. 14(1), pages 35-62, March.
    7. 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.
    8. Tang, Ling & Yu, Lean & Wang, Shuai & Li, Jianping & Wang, Shouyang, 2012. "A novel hybrid ensemble learning paradigm for nuclear energy consumption forecasting," Applied Energy, Elsevier, vol. 93(C), pages 432-443.
    9. Abed, Seraj Y. & Ba-Fail, Abdullah O. & Jasimuddin, Sajjad M., 2001. "An econometric analysis of international air travel demand in Saudi Arabia," Journal of Air Transport Management, Elsevier, vol. 7(3), pages 143-148.
    10. Grosche, Tobias & Rothlauf, Franz & Heinzl, Armin, 2007. "Gravity models for airline passenger volume estimation," Journal of Air Transport Management, Elsevier, vol. 13(4), pages 175-183.
    11. Sims, Christopher A, 1980. "Macroeconomics and Reality," Econometrica, Econometric Society, vol. 48(1), pages 1-48, January.
    12. Sun, Shaolong & Qiao, Han & Wei, Yunjie & Wang, Shouyang, 2017. "A new dynamic integrated approach for wind speed forecasting," Applied Energy, Elsevier, vol. 197(C), pages 151-162.
    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. Xiao, Yi & Liu, John J. & Hu, Yi & Wang, Yingfeng & Lai, Kin Keung & Wang, Shouyang, 2014. "A neuro-fuzzy combination model based on singular spectrum analysis for air transport demand forecasting," Journal of Air Transport Management, Elsevier, vol. 39(C), pages 1-11.
    15. Duval, David Timothy & Schiff, Aaron, 2011. "Effect of air services availability on international visitors to New Zealand," Journal of Air Transport Management, Elsevier, vol. 17(3), pages 175-180.
    16. Park, Yonghwa & Ha, Hun-Koo, 2006. "Analysis of the impact of high-speed railroad service on air transport demand," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 42(2), pages 95-104, March.
    17. Jovicic, Goran & Hansen, Christian Overgaard, 2003. "A passenger travel demand model for Copenhagen," Transportation Research Part A: Policy and Practice, Elsevier, vol. 37(4), pages 333-349, May.
    18. Elton Fernandes & Ricardo Rodrigues Pacheco, 2010. "The causal relationship between GDP and domestic air passenger traffic in Brazil," Transportation Planning and Technology, Taylor & Francis Journals, vol. 33(7), pages 569-581, July.
    Full references (including those not matched with items on IDEAS)

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