关键词: 航空旅客出行指数, 机器学习, 长短期记忆网络, 支持向量回归, K-均值聚类

Abstract: The situation of air passenger travel is of great significance to the construction and operation of civil aviation airports. The travel index of air passengers is defined, and the machine learning method is used to predict the travel index of air passengers. To overcome the insufficient prediction accuracy of a single model, a long short-term memory network (LSTM) and support vector regression (SVR) integrated forecasting model is proposed, and clustering analysis is performed on the forecast result set. The empirical progressive analysis based on airport passengers in Shanghai Airport verifies the feasibility and validity of the forecasting model proposed. The experimental results show that the forecasting model proposed has a higher accuracy than the traditional single forecasting model. At the same time, the forecasting model proposed also has obvious advantages compared with other integrated forecasting models. In addition, based on the K-means algorithm, the travel index of air passengers is clustered and a row rating is given, which provides certain value decision support for airport operation management and travel choice of passengers.

Key words: travel index of air passengers, machine learning, long short-term memory (LSTM), support vector regression (SVR), K-means