Due to the strong propagation causality of delays between airports, this paper proposes a delay prediction model based on a deep graph neural network to study delay prediction from the perspective of an airport network. We regard airports as nodes of a graph network and use a directed graph network to construct airports’ relationship. For adjacent airports, weights of edges are measured by the spherical distance between them, while the number of flight pairs between them is utilized for airports connected by flights. On this basis, a diffusion convolution kernel is constructed to capture characteristics of delay propagation between airports, and it is further integrated into the sequence-to-sequence LSTM neural network to establish a deep learning framework for delay prediction. We name this model as deep graph-embedded LSTM (DGLSTM). To verify the model’s effectiveness and superiority, we utilize the historical delay data of 325 airports in the United States from 2015 to 2018 as the model training set and test set. The experimental results suggest that the proposed method is superior to the existing mainstream methods in terms of accuracy and robustness.

中文翻译：

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用于航班延误预测的深度图嵌入LSTM神经网络方法

由于机场之间延误的强烈传播因果关系，本文提出了一种基于深度图神经网络的延误预测模型，从机场网络的角度研究延误预测。我们将机场视为图网络的节点，并使用有向图网络来构建机场的关系。对于相邻的机场，边缘的权重通过边缘之间的球面距离来衡量，而边缘之间的飞行对数则用于通过航班连接的机场。在此基础上，构造了扩散卷积核以捕获机场之间的延迟传播特征，并将其进一步集成到序列到序列LSTM神经网络中，以建立用于延迟预测的深度学习框架。我们将此模型命名为深图嵌入LSTM（DGLSTM）。为了验证该模型的有效性和优越性，我们将2015年至2018年美国325个机场的历史延误数据用作模型训练集和测试集。实验结果表明，该方法在准确性和鲁棒性方面均优于现有的主流方法。