关键词: 短时客流预测, 图卷积网络, 航空运输, 机场地铁交通

Abstract: The short-term passenger flow prediction of the airport subway is the key to realize the rapid evacuation of airport passengers and the command and dispatch of on-site capacity resources of the terminal. Taking into account the complex spatial structure of the airport and the impact of flight fluctuations, a short-term passenger flow prediction model for airport subways which combined graph convolutional neural network（GCN） and gated convolution（GLU） is established. Based on the graph convolutional neural network（GCN）, the spatial structure relationship between airport path points and subway gate is obtained. At the same time, a combined gated convolution method is proposed to mine the time-varying characteristics of subway passenger flow under flight fluctuations, and effectively capture the volatility of subway passenger flow. Taking the Capital Airport T3 terminal as the research object, after many experiments, the root mean square error, average absolute error and average absolute percentage error of the prediction performance of the graph convolution spatio-temporal prediction model are all smaller than the traditional ARIMA prediction model and the LSTM, STGCN model. The results show that the model can capture the fluctuation relationship between subway passenger flow and flight passenger flow, has high prediction accuracy, and improves the robustness of model prediction.

Key words: short-term passenger flow prediction, graph convolutional network, air transportation, airport subway transportation