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.

中文翻译：

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MAST-GNN：用于空域复杂性预测的多模态自适应时空图神经网络

空域复杂性被定义为综合衡量空中交通运行状况安全性的重要指标。空域复杂性的可靠预测可以为制定空中交通管理策略和资源分配提供实用指导。尽管人们在计算空域复杂性方面付出了巨大的努力，但以前的研究很少能够对空域复杂性数据中的多维和组合时空特征进行建模。在本文中，我们提出了一种多模态自适应时空图神经网络，以同时探索空域扇区网络中的时空依赖性。具体来说，我们设计了一种多模态自适应图卷积模块，以有效学习不同的空间关系，并以数据驱动的方式自适应调整不同空间模式对空域复杂性的影响。为了对动态的长期-短期时间模式进行建模，我们开发了一个具有多时间步长自注意力机制的扩张因果卷积层，以准确预测较长时间范围内的空域复杂性。对现实世界空中交通数据集的大量实验表明，所提出的方法可以利用不同的空间模式在不同的时间模式下实现更高的泛化性能，在所有预测时间范围内都优于最先进的方法。