Highlights This paper proposed a novel deep graph learning framework for airspace complexity prediction that can comprehensively capture the spatio-temporal dependencies between large-scale airspace sectors. This paper constructed a multimodal adaptive graph convolution module that can effectively explore the multiple spatial dependencies and adaptively adjust the impact of different spatial modes on airspace complexity in a data-driven learning manner. This paper designed a temporal convolution module based on a multiple-time-step self-attention mechanism to extract the mixed short- and long-term temporal patterns from both local and global perspectives. This paper constructed a comprehensive dataset from real-world air traffic data within Chinese domestic airspace, ensuring its reliability through rigorous calibration with experienced air traffic controllers.
Abstract Airspace complexity is defined as an essential indicator to comprehensively measure the safety of air traffic operational situations. A reliable prediction of airspace complexity can provide practical guidance for formulating air traffic management strategies and resource allocation. Although extensive efforts have been devoted to computing airspace complexity, previous studies can rarely model the multi-dimensional and combined spatio-temporal features within airspace complexity data. In this paper, we propose a multimodal adaptive spatio-temporal graph neural network to simultaneously explore the spatio-temporal dependencies in the airspace sector network. Specifically, we design a multimodal adaptive graph convolution module to effectively learn the diverse spatial relationships and adaptively adjust the impact of different spatial modes on airspace complexity in a data-driven manner. To model dynamic long-short-term temporal patterns, we develop a dilated causal convolution layer with a multiple-time-step self-attention mechanism to accurately predict airspace complexity over a longer time horizon. Extensive experiments on real-world air traffic datasets show that the proposed approach can harness differing spatial modes in achieving higher generalization performance across different temporal patterns, outperforming state-of-the-art methods in all prediction time horizons.
MAST-GNN: A multimodal adaptive spatio-temporal graph neural network for airspace complexity prediction
2024-02-06
Article (Journal)
Electronic Resource
English
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