基于Graph WaveNet模型的机场网络延误预测

Airport Network Delay Prediction Based on Graph WaveNet Model

文中提出一种基于深度Graph WaveNet(GWN)模型的机场网络延误预测方法,对机场网络整体建模,将其转换为图结构并对网络中所有机场进行离港航班多步延误预测.GWN模型融合时间和空间卷积网络,时间卷积层引入扩展因果卷积和门控机制提升模型效率;空间卷积层采用双向卷积及自适应邻接矩阵充分挖掘延误信息的空间关联性.选择美国51个机场构建机场网络并进行延误预测分析.结果表明:GWN模型对机场未来3天离港航班准点率预测的平均绝对误差分别为4.718%、5.145%和5.240%,显著优于其它基线模型,且对不同量级机场均有稳定的预测表现,在多步预测上具有突出优势.

A method of airport network delay prediction based on depth Graph WaveNet(GWN)model was proposed.The whole airport network was modeled and converted into graph structure,and the multi-step delay prediction of all airports in the network was made.The GWN model integrates time and space convolution networks,and the introduction of extended causal convolution and gating mechanism in the time convolution layer improved the efficiency of the model.Two-way convolution and adaptive adjacency matrix were used in spatial convolution layer to fully explore the spatial relevance of delay information.Fifty-one airports in the United States were selected to build an airport network and carry out delay prediction analysis.The results show that the average absolute errors of GWN model in predicting the on-time rate of departing flights in the next three days are 4.718%,5.145%and 5.240%,respectively,which are significantly better than other baseline models.Moreover,GWN model has stable forecasting performance for airports of different orders of magnitude,and has outstanding advantages in multi-step forecasting.