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

摘要： 机场地铁短时客流预测是实现机场旅客快速疏解、航站楼现场运力资源指挥调度的关键。考虑到机场复杂的空间结构与航班波动的影响，建立基于图卷积神经网络（GCN）和组合门控卷积（GLU）的机场地铁短时客流预测模型。通过图卷积神经网络融合机场空间路径点与地铁口的空间结构关系，同时，设计一种组合门控卷积模块挖掘航班波动下地铁客流的时变特征，有效地捕捉地铁客流的波动性。基于首都机场T3航站楼真实客流数据对模型的有效性进行检验，经多次实验结果表明，提出的时空图卷积短时客流预测模型在均方根误差、平均绝对误差和平均绝对百分比误差均小于传统ARIMA预测模型与深度学习中LSTM、STGCN模型，该模型能捕捉地铁客流与航班客流的波动变化关系，具有较高的预测精度，提高了模型预测的鲁棒性。

关键词: 短时客流预测, 图卷积网络, 航空运输, 机场地铁交通