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Application of Fuzzy-Based Support Vector Regression to Forecast of International Airport Freight Volumes

Author

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  • Cheng-Hong Yang

    (Department of Business Administration, Tainan University of Technology, Tainan 71002, Taiwan
    Department of Electronic Engineering, National Kaohsiung University of Science and Technology, Kaohsiung 80778, Taiwan
    Ph.D. Program in Biomedical Engineering, Kaohsiung Medical University, Kaohsiung 80708, Taiwan
    School of Dentistry, Kaohsiung Medical University, Kaohsiung 80708, Taiwan)

  • Jen-Chung Shao

    (Department of Electronic Engineering, National Kaohsiung University of Science and Technology, Kaohsiung 80778, Taiwan)

  • Yen-Hsien Liu

    (Department of Electronic Engineering, National Kaohsiung University of Science and Technology, Kaohsiung 80778, Taiwan)

  • Pey-Huah Jou

    (Department of Electronic Engineering, National Kaohsiung University of Science and Technology, Kaohsiung 80778, Taiwan)

  • Yu-Da Lin

    (Department of Computer Science and Information Engineering, National Penghu University of Science and Technology, Penghu 880011, Taiwan)

Abstract

As freight volumes increase, airports are likely to require additional infrastructure development, increased air services, and expanded facilities. Prediction of freight volumes could ensure effective investment. Among the computational intelligence models, support vector regression (SVR) has become the dominant modeling paradigm. In this study, a fuzzy-based SVR (FSVR) model was used to solve the freight volume prediction problem in international airports. The FSVR model can use a fuzzy time series of historical traffic changes for predictions. A fuzzy classification algorithm was used for elements of similar levels in the time series to appropriately divide traffic changes into fuzzy sets, generate membership function values, and establish a fuzzy relationship to produce a fuzzy interpolation with a minimal error. A comparison of the FSVR model with other models revealed that the FSVR model had the lowest mean absolute percentage error (all < 2.5%), mean absolute error, and root mean square error for all types of traffic at all the analyzed airports. Fuzzy sets can handle uncertainty and imprecision in time series. Therefore, the prediction accuracy of the entire time series model is improved by taking advantage of SVR and fuzzy sets. By using the highly accurate FSVR model to predict the future growth of air freight volume, airport management could analyze their existing facilities and service capacity to identify operational bottlenecks and plan future development. The FSVR model is the most accurate forecasting model for air traffic forecasting.

Suggested Citation

  • Cheng-Hong Yang & Jen-Chung Shao & Yen-Hsien Liu & Pey-Huah Jou & Yu-Da Lin, 2022. "Application of Fuzzy-Based Support Vector Regression to Forecast of International Airport Freight Volumes," Mathematics, MDPI, vol. 10(14), pages 1-18, July.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:14:p:2399-:d:858678
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    1. Dorian, James P. & Franssen, Herman T. & Simbeck, Dale R., 2006. "Global challenges in energy," Energy Policy, Elsevier, vol. 34(15), pages 1984-1991, October.
    2. Rosa Duarte & Vicente Pinilla & Ana Serrano, 2018. "Factors driving embodied carbon in international trade: a multiregional input–output gravity model," Economic Systems Research, Taylor & Francis Journals, vol. 30(4), pages 545-566, October.
    3. Yang, Youlong & Che, Jinxing & Deng, Chengzhi & Li, Li, 2019. "Sequential grid approach based support vector regression for short-term electric load forecasting," Applied Energy, Elsevier, vol. 238(C), pages 1010-1021.
    4. Abate, Megersa & Christidis, Panayotis & Purwanto, Alloysius Joko, 2020. "Government support to airlines in the aftermath of the COVID-19 pandemic," Journal of Air Transport Management, Elsevier, vol. 89(C).
    5. Mrs. Swarnali A Hannan, 2018. "Revisiting the Determinants of Capital Flows to Emerging Markets--A Survey of the Evolving Literature," IMF Working Papers 2018/214, International Monetary Fund.
    6. Alexander, D.W. & Merkert, R., 2021. "Applications of gravity models to evaluate and forecast US international air freight markets post-GFC," Transport Policy, Elsevier, vol. 104(C), pages 52-62.
    7. Hyndman, Rob J. & Khandakar, Yeasmin, 2008. "Automatic Time Series Forecasting: The forecast Package for R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 27(i03).
    8. Hassani, Hossein & Silva, Emmanuel Sirimal & Antonakakis, Nikolaos & Filis, George & Gupta, Rangan, 2017. "Forecasting accuracy evaluation of tourist arrivals," Annals of Tourism Research, Elsevier, vol. 63(C), pages 112-127.
    9. Hassani, Hossein, 2007. "Singular Spectrum Analysis: Methodology and Comparison," MPRA Paper 4991, University Library of Munich, Germany.
    10. Tai Vovan, 2019. "An improved fuzzy time series forecasting model using variations of data," Fuzzy Optimization and Decision Making, Springer, vol. 18(2), pages 151-173, June.
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    Cited by:

    1. Cheng-Hong Yang & Borcy Lee & Pey-Huah Jou & Yu-Fang Chung & Yu-Da Lin, 2023. "Analysis and Forecasting of International Airport Traffic Volume," Mathematics, MDPI, vol. 11(6), pages 1-19, March.

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