Empirical Exploration of Aircraft Headway Evolution Characteristics in Terminal Maneuvering Area

Air traffic flow analysis relies on understanding time intervals between flying aircraft, known as headways. These intervals are crucial in balancing traffic demand and capacity, guided by air traffic control decisions. Modeling the headway distribution is the key to understanding evolution characteristics of air traffic flow. High dynamics of air traffic flow brings challenge to modeling. To address this, we propose a data-driven solution using a sophisticated data model, a ternary mixed-distribution model. This model, determined through algorithms like Expectation Maximization (EM), effectively captures the complexity of air traffic dynamics. Our experiments with Xiamen Terminal Maneuvering Area (TMA) aircraft trajectory data demonstrate the model's accuracy and reveal vital connections between traffic states and headway evolution. By comparing with other models, it is shown that the ternary mixed-distribution model has the smallest Root mean square errors.