Accurate and reliable aircraft landing time prediction is essential for effective resource allocation in air traffic management. However, the inherent uncertainty of aircraft trajectories and traffic flows poses significant challenges to both prediction accuracy and trustworthiness. Therefore, prediction models should not only provide point estimates of aircraft landing times but also the uncertainties associated with these predictions. Furthermore, aircraft trajectories are frequently influenced by the presence of nearby aircraft through air traffic control interventions such as radar vectoring. Consequently, landing time prediction models must account for multi-agent interactions in the airspace. In this work, we propose a probabilistic multi-agent aircraft landing time prediction framework that provides the landing times of multiple aircraft as distributions. We evaluate the proposed framework using an air traffic surveillance dataset collected from the terminal airspace of the Incheon International Airport in South Korea. The results demonstrate that the proposed model achieves higher prediction accuracy than the baselines and quantifies the associated uncertainties of its outcomes. In addition, the model uncovered underlying patterns in air traffic control through its attention scores, thereby enhancing explainability.

翻译：准确可靠的飞机着陆时间预测对于空中交通管理中有效的资源分配至关重要。然而，飞机轨迹与交通流固有的不确定性对预测的准确性和可信度均构成重大挑战。因此，预测模型不仅应提供飞机着陆时间的点估计，还应提供这些预测相关的不确定性。此外，飞机轨迹常受到附近飞机的影响，这种影响通过空中交通管制干预（如雷达引导）实现。因此，着陆时间预测模型必须考虑空域中的多智能体交互。本研究提出一种概率多智能体飞机着陆时间预测框架，该框架以分布形式提供多架飞机的着陆时间。我们使用从韩国仁川国际机场终端空域收集的空中交通监视数据集对所提框架进行评估。结果表明，所提模型较基线方法实现了更高的预测精度，并量化了其输出的相关不确定性。此外，该模型通过其注意力分数揭示了空中交通管制中的潜在模式，从而增强了可解释性。