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Analysis and Forecasting of International Airport Traffic Volume

Author

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  • Cheng-Hong Yang

    (Department of Information Management, Tainan University of Technology, Tainan 710302, 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)

  • Borcy Lee

    (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-Fang Chung

    (Department of Electrical Engineering, Tunghai University, Taichung 407224, Taiwan)

  • Yu-Da Lin

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

Abstract

Globalization has resulted in increases in air transportation demand and air passenger traffic. With the increases in air traffic, airports face challenges related to infrastructure, air services, and future development. Air traffic forecasting is essential to ensuring appropriate investment in airports. In this study, we combined fuzzy theory with support vector regression (SVR) to develop a fuzzy SVR (FSVR) model for forecasting international airport traffic. This model was used to predict the air traffic volumes at the world’s 10 busiest airports in terms of air traffic in 2018. The predictions were made for the period from August 2014 to December 2019. For fuzzy time series, the developed FSVR model can consider historical air traffic changes. The FSVR model can suitably divide air traffic changes into appropriate fuzzy sets, generate membership function values, and establish fuzzy relations to produce fuzzy interpolated values with minimal errors. Thus, in the prediction of continuous data, the fuzzy data with the smallest errors can be subjected to SVR to find the optimal hyperplane model with the minimum distance to the appropriate support vector sample points. The performance of the proposed model was compared with those of five other models. Of the compared models, the FSVR model exhibited the lowest mean absolute percentage error (MAPE), mean absolute error, and root mean square error for all types of traffic at all of the airports analyzed; all of the MAPE values were below 2.5. The FSVR model can predict future growth trends in air traffic, air passenger flows, aircraft flows, and logistics. An airport authority can use this model to analyze the existing operational facilities and service capacity, find bottlenecks in airport operations, and create a blueprint for future development. The findings revealed that implementing a hybrid modeling approach, specifically the FSVR model, can significantly enhance the performance of the SVR model. The FSVR model allows airlines to predict traffic growth patterns, identify viable new destinations, optimize their schedules or fleet, make accurate marketing decisions, and plan traffic effectively. The FSVR model can guide the timely construction of appropriate airport facilities with accurate predictions. Rapid, cost-effective, efficient, and balanced transportation planning enables the provision of fast, cost-effective, comfortable, safe, and convenient passenger and cargo services while ensuring the proper planning of the airport’s capacity for land-side transportation connections.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:6:p:1483-:d:1100754
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    References listed on IDEAS

    as
    1. 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.
    2. 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).
    3. Eden Xiaoying Jiao & Jason Li Chen, 2019. "Tourism forecasting: A review of methodological developments over the last decade," Tourism Economics, , vol. 25(3), pages 469-492, May.
    4. Choi, Sun & Kim, Young Jin, 2021. "Artificial neural network models for airport capacity prediction," Journal of Air Transport Management, Elsevier, vol. 97(C).
    5. 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.
    6. Dorian, James P. & Franssen, Herman T. & Simbeck, Dale R., 2006. "Global challenges in energy," Energy Policy, Elsevier, vol. 34(15), pages 1984-1991, October.
    7. Cheong Kim & Francis Joseph Costello & Kun Chang Lee, 2019. "Integrating Qualitative Comparative Analysis and Support Vector Machine Methods to Reduce Passengers’ Resistance to Biometric E-Gates for Sustainable Airport Operations," Sustainability, MDPI, vol. 11(19), pages 1-22, September.
    8. Panayotis Christidis & Aris Christodoulou, 2020. "The Predictive Capacity of Air Travel Patterns during the Global Spread of the COVID-19 Pandemic: Risk, Uncertainty and Randomness," IJERPH, MDPI, vol. 17(10), pages 1-15, May.
    9. 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.
    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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