LAUSR

As explainable artificial intelligence (XAI) has grown in popularity, it has become possible to more clearly explain the functional correspondence between airspace and traffic flow, which can alleviate the contradiction between the continuously increasing traffic demand in the terminal area and the limited airspace capacity; this paper studies the three-dimensional sectorization in the terminal area based on the airspace function. First, trajectory clustering is adopted to classify the functions of air traffic flows. Then, a functional sectorization framework is proposed to enable different airspace sectors with varied functions, where specifically the functional consistency objective between the airspace sector and the traversed air traffic flows is proposed. Third, a corresponding efficient algorithm is designed to generate functional sectors with an accurate scope that can well separate different traffic flows. Finally, the proposed method is evaluated by the actual operation datasets in the Shanghai terminal area. The results show that the method proposed in this paper can not only generate three-dimensional sectors with specific functions according to the prevailing traffic flow in the complex terminal area, which is conducive to the construction of controllers’ situational awareness, but also can reduce potential conflicts and traffic density variance, increasing average sector flight time a lot.