Study on the Identification of Terminal Area Traffic Congestion Situation Based on Symmetrical Random Forest

As the demand for air transport continues to increase, air traffic congestion in the terminal area is becoming more and more serious. In order to assist the controller in efficiently handling the symmetrical activities of aircraft take-off or landing and alleviate traffic congestion, this paper proposes a method for identifying traffic congestion situations based on complex networks and a multiclass random forest algorithm with symmetrical characteristics. First, the approach points, departure points, waypoints, and navigation stations are used as nodes, the flight paths as edges, and the busyness of the paths as edge weights to construct a traffic network model for the terminal area. On this basis, five congestion situation recognition indicators are selected from the perspective of network topology, and a symmetric multiclass random forest algorithm is proposed to recognize the congestion situation. Finally, this method is compared with the situation recognition method based on the traditional random forest algorithm. The results of the simulation experiment show that compared with the traditional random forest algorithm, the proposed recognition model improves the recognition accuracy by 17.5%, can better handle symmetry information, and can accurately determine the traffic congestion situation in the terminal area.