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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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    References listed on IDEAS

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    Cited by:

    1. Di Zhang & Xinyuan Li & Chengpeng Wan & Jie Man, 2024. "A novel hybrid deep-learning framework for medium-term container throughput forecasting: an application to China’s Guangzhou, Qingdao and Shanghai hub ports," Maritime Economics & Logistics, Palgrave Macmillan;International Association of Maritime Economists (IAME), vol. 26(1), pages 44-73, March.
    2. 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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