Business School，University of Shanghai for Science and Technology
National Natural Science Foundation of China
航空旅客出行的情况对民用航空机场建设与运营具有重大意义，针对航空旅客出行情况的预测研究，首先，定义一种航空旅客出行指数，通过K-means聚类方法对航空旅客出行指数进行分级；其次，基于互信息与相关性原理，选取航空旅客出行情况关键影响特征因子，提出一种基于关键影响因子与航空旅客出行指数互信息的MI-SVR(Mutual Information - Support Vector Regression)机器学习预测模型；最后，通过上海机场旅客出行指数预测实验对模型进行验证，实验结果显示MI-SVR模型具有可行性与有效性，同时，相比传统的预测模型预测效果更优。此外，实验结果也表明各模型基于互信息引入影响因子进行预测相对仅基于历史数据进行独立预测误差更小。本研究结果有助于提升机场建设及运营管理水平，同时，也可辅助人们选择通过民航交通方式出行的时段。
Air passenger travel is of great significance to the construction and operation of civil aviation airports. This paper studies the prediction of air passenger index, the main research work is as follows: Firstly, the air passenger index is defined, and the air passenger index is classified by K-means clustering method. Secondly, based on the principle of mutual information and correlation, the key influencing factors of air passenger index are selected, this work presents a method of MI-SVR (Mutual Information - Support Vector Regression) machine learning model based on mutual information , which estimates between the key influencing factors and air passenger index, this model(MI-SVR) is used to predict air passenger index. Finally, the model is validated by passenger throughput data of Shanghai Airport, the experimental results show that the MI-SVR model is feasible and effective, compared with classical models, IM-SVR model has better prediction effect. In addition, the experimental results also show that the prediction effect of each model is better after introducing influence factors based on mutual information. Overall, the study is helpful to the construction and operation of airport , At the same time, it can also help people choose the time to travel by air.