IDEAS home Printed from https://ideas.repec.org/a/eee/jaitra/v83y2020ics0969699719302261.html
   My bibliography  Save this article

Forecasting air passenger demand with a new hybrid ensemble approach

Author

Listed:
  • Jin, Feng
  • Li, Yongwu
  • Sun, Shaolong
  • Li, Hongtao

Abstract

Analyzing and modeling passenger demand dynamic, which has important implications on the management and the operation in the entire aviation industry, are deemed to be a tough challenge. Air passenger demand, however, exhibits consistently complex non-linearity and non-stationarity. To capture more precisely the aforementioned complex behavior, this paper proposes a hybrid approach VMD-ARMA/KELM-KELM for the short-term forecasting, which consists of variational mode decomposition (VMD), autoregressive moving average model (ARMA) and kernel extreme learning machine (KELM). First, VMD is adopted to decompose the original data into several mode functions so as to reduce their complexity. Then, the unit root test (ADF test) is employed to classify all the modes into the stable and unstable series. Meanwhile, the ARMA and the KELM models are used to forecast both the stationary and non-stationary components, respectively. Lastly, the final result is integrated by another KELM model incorporating the forecasting results of all components. In order to prove and verify the feasibility and robustness of the proposed approach, the passenger demands of Beijing, Guangzhou and Pudong airports are introduced to test the performance. Also, the experimental results show that the novel approach does have a more obviously advantage than other benchmark models regarding both accuracy and robustness analysis. Therefore, this approach can be utilized as a convincing tool for the air passenger demand forecasting.

Suggested Citation

  • Jin, Feng & Li, Yongwu & Sun, Shaolong & Li, Hongtao, 2020. "Forecasting air passenger demand with a new hybrid ensemble approach," Journal of Air Transport Management, Elsevier, vol. 83(C).
  • Handle: RePEc:eee:jaitra:v:83:y:2020:i:c:s0969699719302261
    DOI: 10.1016/j.jairtraman.2019.101744
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0969699719302261
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.jairtraman.2019.101744?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.

    References listed on IDEAS

    as
    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. 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.
    3. Zhu, Jiaming & Wu, Peng & Chen, Huayou & Liu, Jinpei & Zhou, Ligang, 2019. "Carbon price forecasting with variational mode decomposition and optimal combined model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 519(C), pages 140-158.
    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. Wang, Jianzhou & Heng, Jiani & Xiao, Liye & Wang, Chen, 2017. "Research and application of a combined model based on multi-objective optimization for multi-step ahead wind speed forecasting," Energy, Elsevier, vol. 125(C), pages 591-613.
    6. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    7. Van Arem, Bart & Kirby, Howard R. & Van Der Vlist, Martie J. M. & Whittaker, Joe C., 1997. "Recent advances and applications in the field of short-term traffic forecasting," International Journal of Forecasting, Elsevier, vol. 13(1), pages 1-12, March.
    8. 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.
    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. Hakim, Md Mahbubul & Merkert, Rico, 2016. "The causal relationship between air transport and economic growth: Empirical evidence from South Asia," Journal of Transport Geography, Elsevier, vol. 56(C), pages 120-127.
    11. 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.
    12. Li, Chaoshun & Xiao, Zhengguang & Xia, Xin & Zou, Wen & Zhang, Chu, 2018. "A hybrid model based on synchronous optimisation for multi-step short-term wind speed forecasting," Applied Energy, Elsevier, vol. 215(C), pages 131-144.
    13. Chin, Anthony T.H & Tay, John H, 2001. "Developments in air transport: implications on investment decisions, profitability and survival of Asian airlines," Journal of Air Transport Management, Elsevier, vol. 7(5), pages 319-330.
    14. 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.
    15. Baker, Douglas & Merkert, Rico & Kamruzzaman, Md., 2015. "Regional aviation and economic growth: cointegration and causality analysis in Australia," Journal of Transport Geography, Elsevier, vol. 43(C), pages 140-150.
    16. Shao, Zhen & Gao, Fei & Yang, Shan-Lin & Yu, Ben-gong, 2015. "A new semiparametric and EEMD based framework for mid-term electricity demand forecasting in China: Hidden characteristic extraction and probability density prediction," Renewable and Sustainable Energy Reviews, Elsevier, vol. 52(C), pages 876-889.
    17. 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.
    18. Zhao, Jing & Guo, Zhen-Hai & Su, Zhong-Yue & Zhao, Zhi-Yuan & Xiao, Xia & Liu, Feng, 2016. "An improved multi-step forecasting model based on WRF ensembles and creative fuzzy systems for wind speed," Applied Energy, Elsevier, vol. 162(C), pages 808-826.
    19. 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.
    20. 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.
    21. Dantas, Tiago Mendes & Cyrino Oliveira, Fernando Luiz & Varela Repolho, Hugo Miguel, 2017. "Air transportation demand forecast through Bagging Holt Winters methods," Journal of Air Transport Management, Elsevier, vol. 59(C), pages 116-123.
    22. J. D. Bermudez & J. V. Segura & E. Vercher, 2007. "Holt-Winters Forecasting: An Alternative Formulation Applied to UK Air Passenger Data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 34(9), pages 1075-1090.
    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. Huanyin Su & Shuting Peng & Shanglin Mo & Kaixin Wu, 2022. "Neural Network-Based Hybrid Forecasting Models for Time-Varying Passenger Flow of Intercity High-Speed Railways," Mathematics, MDPI, vol. 10(23), pages 1-21, December.
    2. Bin Chen & Jin Wu, 2022. "Predicting Model for Air Transport Demand under Uncertainties Based on Particle Filter," Sustainability, MDPI, vol. 14(24), pages 1-13, December.
    3. Tillmann, Andreas M. & Joormann, Imke & Ammann, Sabrina C.L., 2023. "Reproducible air passenger demand estimation," Journal of Air Transport Management, Elsevier, vol. 112(C).
    4. Wang, Zhanwei & Song, Woon-Kyung, 2020. "Sustainable airport development with performance evaluation forecasts: A case study of 12 Asian airports," Journal of Air Transport Management, Elsevier, vol. 89(C).
    5. Tirtha, Sudipta Dey & Bhowmik, Tanmoy & Eluru, Naveen, 2023. "Understanding the factors affecting airport level demand (arrivals and departures) using a novel modeling approach," Journal of Air Transport Management, Elsevier, vol. 106(C).
    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. Tascón, Diana C. & Díaz Olariaga, Oscar, 2021. "Air traffic forecast and its impact on runway capacity. A System Dynamics approach," Journal of Air Transport Management, Elsevier, vol. 90(C).
    8. Gunter, Ulrich & Zekan, Bozana, 2021. "Forecasting air passenger numbers with a GVAR model," Annals of Tourism Research, Elsevier, vol. 89(C).
    9. Hu, Yi-Chung, 2023. "Air passenger flow forecasting using nonadditive forecast combination with grey prediction," Journal of Air Transport Management, Elsevier, vol. 112(C).

    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. Tascón, Diana C. & Díaz Olariaga, Oscar, 2021. "Air traffic forecast and its impact on runway capacity. A System Dynamics approach," Journal of Air Transport Management, Elsevier, vol. 90(C).
    3. Nieto, María Rosa & Carmona-Benítez, Rafael Bernardo, 2018. "ARIMA + GARCH + Bootstrap forecasting method applied to the airline industry," Journal of Air Transport Management, Elsevier, vol. 71(C), pages 1-8.
    4. 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.
    5. de Paula, R.O. & Silva, L.R. & Vilela, M.L. & Cruz, R.O.M., 2019. "Forecasting passenger movement for Brazilian airports network based on the segregation of primary and secondary demand applied to Brazilian civil aviation policies planning," Transport Policy, Elsevier, vol. 77(C), pages 23-29.
    6. 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.
    7. Dantas, Tiago Mendes & Cyrino Oliveira, Fernando Luiz & Varela Repolho, Hugo Miguel, 2017. "Air transportation demand forecast through Bagging Holt Winters methods," Journal of Air Transport Management, Elsevier, vol. 59(C), pages 116-123.
    8. Gizem Kaya & Umut Aydın & Burç Ülengin, 2023. "A Comparison of Forecasting Performance of PPML and OLS estimators: The Gravity Model in the Air Cargo Market," EKOIST Journal of Econometrics and Statistics, Istanbul University, Faculty of Economics, vol. 0(39), pages 112-128, December.
    9. Hanson, Daniel & Toru Delibasi, Tuba & Gatti, Matteo & Cohen, Shamai, 2022. "How do changes in economic activity affect air passenger traffic? The use of state-dependent income elasticities to improve aviation forecasts," Journal of Air Transport Management, Elsevier, vol. 98(C).
    10. Gunter, Ulrich & Zekan, Bozana, 2021. "Forecasting air passenger numbers with a GVAR model," Annals of Tourism Research, Elsevier, vol. 89(C).
    11. 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).
    12. 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).
    13. Yang, Wendong & Wang, Jianzhou & Niu, Tong & Du, Pei, 2019. "A hybrid forecasting system based on a dual decomposition strategy and multi-objective optimization for electricity price forecasting," Applied Energy, Elsevier, vol. 235(C), pages 1205-1225.
    14. Tian, Chengshi & Hao, Yan & Hu, Jianming, 2018. "A novel wind speed forecasting system based on hybrid data preprocessing and multi-objective optimization," Applied Energy, Elsevier, vol. 231(C), pages 301-319.
    15. Hu, Yi-Chung, 2023. "Air passenger flow forecasting using nonadditive forecast combination with grey prediction," Journal of Air Transport Management, Elsevier, vol. 112(C).
    16. Truong, Dothang, 2021. "Estimating the impact of COVID-19 on air travel in the medium and long term using neural network and Monte Carlo simulation," Journal of Air Transport Management, Elsevier, vol. 96(C).
    17. Wang, Yue & Wong, Collin Wai Hung & Cheung, Tommy King-Yin & Wu, Edmund Yangming, 2021. "How influential factors affect aviation networks: A Bayesian network analysis," Journal of Air Transport Management, Elsevier, vol. 91(C).
    18. 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.
    19. Balli, Hatice Ozer & Tsui, Wai Hong Kan & Balli, Faruk, 2019. "Modelling the volatility of international visitor arrivals to New Zealand," Journal of Air Transport Management, Elsevier, vol. 75(C), pages 204-214.
    20. Mohammadian, Iman & Abareshi, Ahmad & Abbasi, Babak & Goh, Mark, 2019. "Airline capacity decisions under supply-demand equilibrium of Australia’s domestic aviation market," Transportation Research Part A: Policy and Practice, Elsevier, vol. 119(C), pages 108-121.

    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:eee:jaitra:v:83:y:2020:i:c:s0969699719302261. 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: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/journal-of-air-transport-management/ .

    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.