| 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
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Accurate flight delay prediction is one of the most important preventive measures for the increasingly frequent airport flight delays. The airport network is transformed from irregular topological structure to regular network structure by spectral convolution. The graph convolutional network (GCN) and gated linear unit (GLU) are used to capture spatio-temporal correlation in the network and form spatio-temporal convolutional blocks. A spatio-temporal graph convolutional neural network (STGCN) is proposed to predict the departure flight delay. 51 major airports in the United States are selected to construct the airport network, and the on-time departure rate is predicted to carry out the example verification. The results show that when the prediction window is 1 day, the mean absolute error (MAE) of STGCN decreased by 18.19%, 10.45% and 6.24%, respectively compared with the historical average (HA) method, long short-term memory (LSTM) and stacked autoencoders (SAEs). When the forecast window is 2 days, MAE decreased by 9.93%, 3.96% and 4.37%; When the forecast window is 3 days, MAE decreased by 7.02%, 2.47% and 9.20%. The example proves that STGCN can improve the accuracy of delay prediction and provide reference and guidance for airport delay decision.
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