Probabilistic Multi-Agent Aircraft Landing Time Prediction

TL;DR

This paper introduces a probabilistic multi-agent framework for aircraft landing time prediction that accounts for uncertainties and interactions, improving accuracy and explainability in air traffic management.

Contribution

The work presents a novel probabilistic multi-agent model that predicts aircraft landing times as distributions, incorporating interactions and uncertainties for better accuracy and interpretability.

Findings

Achieves higher prediction accuracy than baseline models.

Quantifies uncertainties associated with landing time predictions.

Reveals air traffic control patterns through attention mechanisms.

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.