Multi-agent Aircraft Estimated Time of Arrival Prediction in Terminal Airspace

Accurate Estimated Time of Arrival (ETA) predictions have become increasingly important in modern air traffic management systems, which rely on scheduling and metering aircraft based on their remaining flight time in order to ensure safety and efficiency. Most existing ETA prediction methods have focused on a single aircraft which is assumed to be independent. However, the precise time of arrival for a flight in terminal airspace is significantly influenced by both air traffic conditions and decisions made by human operators in response to these conditions. Therefore, this paper proposes a multi-agent ETA prediction method that accounts for the current air traffic situation and captures the decision made by air traffic controllers. This approach relies on an agent-aware attention mechanism, which enables a more comprehensive understanding of traffic situations, resulting in improved prediction accuracy. The proposed method is demonstrated with real air traffic surveillance data recorded at Incheon International Airport, South Korea. The experimental results show the accuracy of the proposed method is superior compared to existing algorithms. In addition, the test results of the aircraft landing sequence present the potential applicability to the aircraft sequencing problem.