概率多智能体飞机着陆时间预测框架

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Abstract: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.

Comments:	Revised version; experiments were rerun after correcting a data leakage issue
Subjects:	Multiagent Systems (cs.MA); Machine Learning (cs.LG)
Cite as:	arXiv:2512.08281 [cs.MA]
	(or arXiv:2512.08281v3 [cs.MA] for this version)
	https://doi.org/10.48550/arXiv.2512.08281 arXiv-issued DOI via DataCite

Submission history

From: Seokbin Yoon [view email]
[v1] Tue, 9 Dec 2025 06:27:26 UTC (4,170 KB)
[v2] Tue, 1 Sep 2026 00:23:06 UTC (4,170 KB)
[v3] Thu, 8 Oct 2026 18:31:19 UTC (3,264 KB)