Air Traffic Flow Pattern Recognition and Analysis in Terminal Area Based on the Geodesic Distance.

An accurate recognition method of the air traffic flow pattern is proposed based on historical flight trajectory data to understand the spatial distribution of air traffic flow and improve the airspace utilization in the terminal area. Due to the high dimensionality of flight trajectory data, we establish a trajectory similarity model based on the geodesic distance and use an improved spectral clustering algorithm to classify the flight trajectory sample data. An improved algorithm based on the minimum spanning tree is presented to extract the skeleton similarity between the tracks and obtain the model of the prevalent traffic flow. The experimental results show that the method can accurately divide 1070 flight paths into 5 categories and extract 7 prevalent traffic flows, exhibiting strong robustness to abnormal trajectories and noise. [ABSTRACT FROM AUTHOR]