Version 1
: Received: 18 August 2020 / Approved: 20 August 2020 / Online: 20 August 2020 (08:31:36 CEST)
How to cite:
Xiong, H.; Fan, C.; ANTWI, C.O.; yang, Y.; fan, X. An Integrated Approach to Air Passenger Index Prediction: Mutual Information Principle and Support Vector Regression (MI-Svr) Blended Model. Preprints2020, 2020080448. https://doi.org/10.20944/preprints202008.0448.v1
Xiong, H.; Fan, C.; ANTWI, C.O.; yang, Y.; fan, X. An Integrated Approach to Air Passenger Index Prediction: Mutual Information Principle and Support Vector Regression (MI-Svr) Blended Model. Preprints 2020, 2020080448. https://doi.org/10.20944/preprints202008.0448.v1
Xiong, H.; Fan, C.; ANTWI, C.O.; yang, Y.; fan, X. An Integrated Approach to Air Passenger Index Prediction: Mutual Information Principle and Support Vector Regression (MI-Svr) Blended Model. Preprints2020, 2020080448. https://doi.org/10.20944/preprints202008.0448.v1
APA Style
Xiong, H., Fan, C., ANTWI, C.O., yang, Y., & fan, X. (2020). An Integrated Approach to Air Passenger Index Prediction: Mutual Information Principle and Support Vector Regression (MI-Svr) Blended Model. Preprints. https://doi.org/10.20944/preprints202008.0448.v1
Chicago/Turabian Style
Xiong, H., yun yang and Xiaomao fan. 2020 "An Integrated Approach to Air Passenger Index Prediction: Mutual Information Principle and Support Vector Regression (MI-Svr) Blended Model" Preprints. https://doi.org/10.20944/preprints202008.0448.v1
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. Based on the 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 passenger throughput data of the Shanghai Pudong International Airport, China. The experimental results prove the model feasibility and effectiveness by comparing them with conventional methods, such as ARIMA, LSTM, and other machine learning models, outperformed by the MI-SVR model. Besides, it is shown that the prediction effect of each model could be improved by introducing influencing factors based on mutual information. The main findings are considered instrumental to the airport operation and air traffic optimization.
Keywords
airport operation and management; air passenger index(API) prediction; machine learning(ML); mutual information(MI); support vector regression (SVR); K-Means
Subject
Computer Science and Mathematics, Information Systems
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
