Area Navigation Terminal Airspace Complexity Estimation for Arrivals

Modern navigation systems such as Area Navigation (RNAV) yield new challenges for developing data-driven algorithms and new perspectives in defining the safety and complexity of the terminal airspace due to the complicated maneuvers of aircraft. In this paper, we propose a complexity estimation framework for RNAV terminal airspace. The framework integrates our previously developed algorithms for trajectory pattern identification, multi-agent trajectory prediction, and Gaussian mixture model-based anomaly detection. All algorithms are developed to be implemented in the complex situation of RNAV terminal airspace. The estimated complexity prompts researchers and air traffic controllers to investigate situations where the complexity is abnormally high for potential risks or operational errors. The proposed complexity estimation framework is tested with real air traffic surveillance data recorded in Incheon International Airport, South Korea.