基于时空序列的Conv-LSTM航班延误预测模型

精准的航班延误预测结果可以为大面积航班延误的预防提供巨大的参考价值。航班延误预测是在特定空间下做时间序列预测，然而目前已有预测方法多为两种或多种算法的结合，存在算法间的融合问题。针对上述问题，提出了一种综合考虑时空序列的卷积长短时记忆（Conv-LSTM）网络航班延误预测模型。所提模型在长短时记忆（LSTM）网络提取时间特征的基础上，将网络的输入和权重矩阵进行卷积来提取空间特征，从而充分利用数据集包含的时间和空间信息。实验结果表明，与LSTM、仅考虑空间信息的卷积神经网络（CNN）模型相比，Conv-LSTM模型的准确率分别提高了0.65个百分点和2.36个百分点。由此可见，同时考虑时空特性可以在航班延误问题中获得更精确的预测结果。此外，基于所提模型设计并实现了基于浏览器/服务器（B/S）架构的航班延误分析系统，并且该系统也可以应用于空中交通管理局流量控制中心。

Flight delay prediction model based on Conv-LSTM with spatiotemporal sequence

The accurate flight delay prediction results can provide a great reference value for the prevention of large-scale flight delays. The flight delays prediction is a time-series prediction in a specific space， however most of the existing prediction methods are the combination of two or more algorithms， and there is a problem of fusion between algorithms. In order to solve the problem above， a Convolutional Long Short-Term Memory （Conv-LSTM） network flight delay prediction model was proposed that considers the temporal and spatial sequences comprehensively. In this model， on the basis that the temporal features were extracted by Long Short-Term Memory （LSTM） network， the input of the network and the weight matrix were convolved to extract spatial features， thereby making full use of the temporal and spatial information contained in the dataset. Experimental results show that the accuracy of the Conv-LSTM model is improved by 0.65 percentage points compared with LSTM， and it is 2.36 percentage points higher than that of the Convolutional Neural Network （CNN） model that only considers spatial information. It can be seen that with considering the temporal and spatial characteristics at the same time， more accurate prediction results can be obtained in the flight delay problem. In addition， based on the proposed model， a flight delay analysis system based on Browser/Server （B/S） architecture was designed and implemented， which can be applied to the air traffic administration flow control center.