A graph multi-attention network for predicting airport delays

Hongfeng Zheng, Ziming Wang, Chuanpan Zheng, Yanjun Wang, Xiaoliang Fan, Wei Cong and Minghua Hu

Transportation Research Part E: Logistics and Transportation Review, 2024, vol. 181, issue C

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.

Keywords: Airport networks; Flight delays; Multi-attention network; Delay prediction; Air transportation (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1016/j.tre.2023.103375

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