A graph multi-attention network for predicting airport delays

Abstract

Predicting airport delays is of great importance for aviation operations, from the development of effective air traffic management strategies to the reallocation of airline resources. In this paper, the long-term prediction of the next 24 h of network-wide delays is investigated. The sensitivity to error propagation over long time periods as well as dynamic spatial correlations and non-linear temporal correlations of the aviation network are considered. An external impact modeling module is introduced to account for the influence of weather on flight delay patterns. A Spatial-Temporal Gated Multi-Attention Graph Network (STGMAGNet) considering external impact to predict airport delays is then developed. We validate our model on a flight delays dataset collected from the Bureau of Transportation Statistics of US, covering January 1, 2019, to December 31, 2019. In long-term (input-24-predict-24 setting) forecasting, STGMAGNet provides state-of-the-art accuracy, with a MAE reduction of at least 21% averaged in arrival delay prediction, 18% averaged MAE reduction in departure delay prediction compared to MLP, LSTM, Seq2Seq and Transformer. Our model can enable aviation management to shift from a reactive to proactive approach, thus enhancing its operational efficiency and overall performance.