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基于时空图卷积神经网络的离港航班延误预测

姜雨 陈名扬 袁琪 戴垚宇

姜雨,陈名扬,袁琪,等. 基于时空图卷积神经网络的离港航班延误预测[J]. 北京航空航天大学学报,2023,49(5):1044-1052 doi: 10.13700/j.bh.1001-5965.2021.0415
引用本文: 姜雨,陈名扬,袁琪,等. 基于时空图卷积神经网络的离港航班延误预测[J]. 北京航空航天大学学报,2023,49(5):1044-1052 doi: 10.13700/j.bh.1001-5965.2021.0415
JIANG Y,CHEN M Y,YUAN Q,et al. Departure flight delay prediction based on spatio-temporal graph convolutional networks[J]. Journal of Beijing University of Aeronautics and Astronautics,2023,49(5):1044-1052 (in Chinese) doi: 10.13700/j.bh.1001-5965.2021.0415
Citation: JIANG Y,CHEN M Y,YUAN Q,et al. Departure flight delay prediction based on spatio-temporal graph convolutional networks[J]. Journal of Beijing University of Aeronautics and Astronautics,2023,49(5):1044-1052 (in Chinese) doi: 10.13700/j.bh.1001-5965.2021.0415

基于时空图卷积神经网络的离港航班延误预测

doi: 10.13700/j.bh.1001-5965.2021.0415
基金项目: 国家自然科学基金(U1933118,U2033205,71971114)
详细信息
    通讯作者:

    E-mail:jiangyu07@nuaa.edu.cn

  • 中图分类号: V351

Departure flight delay prediction based on spatio-temporal graph convolutional networks

Funds: National Natural Science Foundation of China (U1933118,U2033205,71971114)
More Information
  • 摘要:

    对于日益频发的机场航班延误,精准的航班延误预测是最重要的防范措施之一。通过谱图卷积将机场网络从不规则的图结构转换为规则的网络结构,利用图卷积神经网络(GCN)和门控线性单元(GLU)捕获网络中的时空相关性并组成时空卷积块,提出可以预测离港航班延误状况的时空图卷积神经网络(STGCN)。遴选美国51座枢纽机场构建机场网络,并预测未来一段时间内的机场离港准点率以检验STGCN用于预测航班延误的可行性。结果表明:当预测窗口为1天时,STGCN预测结果的平均绝对误差(MAE)相对于历史平均(HA)法、长短期记忆循环神经网络(LSTM)、堆栈自编码器(SAEs)分别下降了18.19%、10.45%、6.24%;当预测窗口为2天时,MAE分别下降了9.93%、3.96%、4.37%;当预测窗口为3天时,MAE分别下降了7.02%、2.47%、9.20%。实例证明STGCN相比传统模型能够显著提升航班延误预测的精度,并为机场制定延误决策提供参考指导。

     

  • 图 1  机场网络图结构的网络数据

    Figure 1.  Network data of airport network graph structure

    图 2  STGCN结构

    Figure 2.  Architecture of STGCN

    图 3  机场网络

    Figure 3.  Airport network

    图 4  机场网络邻接矩阵${\boldsymbol{W}}$可视化

    Figure 4.  Visualization of adjacency matrix ${\boldsymbol{W}}$ in airport network

    图 5  不同预测模型的预测曲线

    Figure 5.  Prediction curves of different forecasting models

    图 6  不同机场的预测结果

    Figure 6.  Prediction results of different airports

    图 7  三种延误程度下的各机场预测误差比较

    Figure 7.  Comparison of forecast errors in airports under three delay levels

    图 8  不同测试日的预测结果

    Figure 8.  Predicted results on different test days

    表  1  机场航班延误数据示例

    Table  1.   Example of airport flight delay data

    数据特征示例
    2019
    1
    13
    机场代码AUS
    机场离港准点率/%83.83
    下载: 导出CSV

    表  2  不同预测模型预测结果的评价指标对比

    Table  2.   Comparison of evaluation indexes for prediction results by different forecasting models

    模型MAEMAPE/%RMSE
    1 d2 d3 d1 d2 d3 d1 d2 d3 d
    HA5.8995.8995.8997.8987.8987.8987.9917.9917.991
    LSTM5.3895.5325.6247.1057.3247.4437.3877.6337.741
    SAEs5.1475.5566.0416.8717.3177.88 7.2687.5728.101
    STGCN4.8265.3135.4856.3997.0697.3076.8427.4677.689
    下载: 导出CSV

    表  3  STGCN与SAEs预测结果评价指标比较

    Table  3.   Comparison of evaluation indexes between STGCN and SAEs

    挑选测试日MAEMAPE/%RMSE
    STGCNSAEsSTGCNSAEsSTGCNSAEs
    淡季中延误日5.316.337.45 8.936.33 7.06
    淡季低延误日4.886.315.55 7.146.39 8.24
    旺季中延误日5.229.018.8314.837.6511.95
    旺季低延误日3.334.724.02 5.574.6211.95
    下载: 导出CSV
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出版历程
  • 收稿日期:  2021-07-22
  • 录用日期:  2021-10-15
  • 网络出版日期:  2021-10-28
  • 整期出版日期:  2023-05-31

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