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A Novel Approach to Air Passenger Index Prediction: Based on Mutual Information Principle and Support Vector Regression Blended Model

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  • Honglin Xiong
  • Chongjun Fan
  • Hongmin Chen
  • Yun Yang
  • Collins Opoku ANTWI
  • Xiaomao Fan

Abstract

Air passenger traffic prediction is crucial for the effective operation of civil aviation airports. Despite some progress in this field, the prediction accuracy and methods need further improvement. This paper proposes an integrated approach to the prediction of air passenger index as follows. Firstly, the air passenger index is defined and classified by the K-means clustering method. And then, based on mutual information (MI) principle, the information entropy is used to analyze and select the key influencing factors of air passenger travel. By incorporating the MI principle into the support vector regression (SVR) framework, this paper presents an innovative MI-SVR machine learning model used to predict the air passenger index. Finally, the proposed model is validated by air passenger throughput data of the Shanghai Pudong International Airport (PVG), China. The experimental results prove MI-SVR model feasibility and effectiveness by comparing them with conventional methods, such as ARIMA, LSTM, and other machine learning models. Besides, it is shown that the prediction effect of each model could be improved by introducing influencing factors based on MI. The main findings are considered instrumental to the airport operation and air traffic optimization.

Suggested Citation

  • Honglin Xiong & Chongjun Fan & Hongmin Chen & Yun Yang & Collins Opoku ANTWI & Xiaomao Fan, 2022. "A Novel Approach to Air Passenger Index Prediction: Based on Mutual Information Principle and Support Vector Regression Blended Model," SAGE Open, , vol. 12(1), pages 21582440211, January.
    • Handle: RePEc:sae:sagope:v:12:y:2022:i:1:p:21582440211071102
    DOI: 10.1177/21582440211071102
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    References listed on IDEAS

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    Cited by:

    1. Zhuo Chen & Kang Tian, 2022. "Optimization of Evaluation Indicators for Driver’s Traffic Literacy: An Improved Principal Component Analysis Method," SAGE Open, , vol. 12(2), pages 21582440221, June.

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