| Citation: | LIU X L，GUO M J，LI Z. Combined prediction method of flight delay based on attention-based adaptive graph convolution-gated recurrent unit[J]. Journal of Beijing University of Aeronautics and Astronautics，2025，51（1）：30-42 （in Chinese） doi: 10.13700/j.bh.1001-5965.2022.0990
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Aiming at the problem of the difficult extraction of spatio-temporal dynamic correlation of flight delay data in a flight delay prediction model, a type of flight delay prediction model based on an attention-based adaptive graph convolution-gated recurrent unit (AAGC-GRU) is proposed. Firstly, the airport network topology graph is constructed with the airport as the node. When combined with the spatial attention mechanism and adaptive graph convolution, it improves the model’s autonomous mining of the spatial dynamic correlation of the airport network and compensates for the over-reliance of graph convolution on prior knowledge. Secondly, GRU was used to obtain the temporal dependence of historical flight delay data, and the time attention mechanism was introduced to automatically allocate the influence weight of data at different time steps, so as to fully capture the impact degree of data at different moments. Then, the fully connected layer is used to obtain the flight delay prediction results. Finally, the experiments are conducted on the on-time departure rate dataset of the American large airport network. In terms of mean absolute error, root mean square error, and mean absolute percentage error, the AAGC-GRU model outperforms the gradient boosting regression tree, gated recurrent unit model, spatio-temporal graph convolutional neural network, and other baseline models.
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