基于Graph WaveNet模型的机场网络延误预测  

Airport Network Delay Prediction Based on Graph WaveNet Model

作　　者：姜雨[1] 戴垚宇 刘振宇 吴薇薇[1] 顾欣 JIANG Yu;DAI Yaoyu;LIU Zhenyu;WU Weiwei;GU Xin(College of Civil Aviation,Nanjing University of Aeronautics and Astronautics,Nanjing 211106,China;Beijing Key Laboratory of Traffic Engineering,Beijing University of Technology,Beijing 100124,China)

机构地区：[1]南京航空航天大学民航学院,南京211106 [2]北京工业大学北京市交通工程重点实验室,北京100124

出　　处：《武汉理工大学学报（交通科学与工程版）》2023年第5期775-780,共6页Journal of Wuhan University of Technology（Transportation Science & Engineering）

基　　金：国家自然科学基金(U1933118,U2033205)。

摘　　要：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.

关 键 词：航班延误预测 Graph WaveNet模型 机场网络 深度学习 

分 类 号：U8[交通运输工程]