Flight trajectory prediction for multiple aircraft is essential and provides critical insights into how aircraft navigate within current air traffic flows. However, predicting multi-agent flight trajectories is inherently challenging. One of the major difficulties is modeling both the individual aircraft behaviors over time and the complex interactions between flights. Generating explainable prediction outcomes is also a challenge. Therefore, we propose a Multi-Agent Inverted Transformer, MAIFormer, as a novel neural architecture that predicts multi-agent flight trajectories. The proposed framework features two key attention modules: (i) masked multivariate attention, which captures spatio-temporal patterns of individual aircraft, and (ii) agent attention, which models the social patterns among multiple agents in complex air traffic scenes. We evaluated MAIFormer using a real-world automatic dependent surveillance-broadcast flight trajectory dataset from the terminal airspace of Incheon International Airport in South Korea. The experimental results show that MAIFormer achieves the best performance across multiple metrics and outperforms other methods. In addition, MAIFormer produces prediction outcomes that are interpretable from a human perspective, which improves both the transparency of the model and its practical utility in air traffic control.

翻译：多飞行器轨迹预测至关重要，能为理解当前空中交通流中飞行器的导航方式提供关键见解。然而，多智能体飞行轨迹预测本身具有挑战性。主要困难之一在于同时建模飞行器随时间变化的个体行为以及航班间复杂的交互作用。生成可解释的预测结果同样是一大挑战。为此，我们提出一种新颖的神经架构——多智能体逆向Transformer（MAIFormer），用于预测多智能体飞行轨迹。该框架包含两个关键注意力模块：（i）掩码多元注意力，用于捕捉个体飞行器的时空模式；（ii）智能体注意力，用于建模复杂空中交通场景中多智能体间的社会交互模式。我们使用来自韩国仁川国际机场终端空域的真实自动相关监视广播飞行轨迹数据集对MAIFormer进行了评估。实验结果表明，MAIFormer在多项指标上均取得最佳性能，且优于其他方法。此外，MAIFormer生成的预测结果可从人类视角进行解释，这既提升了模型的透明度，也增强了其在空中交通管制中的实际应用价值。