Abstract: With the development of global positioning and radar technology,more and more trajectory data can be collected.In particular,trajectories generated by aircrafts,ships,migratory birds are complicated and varied,and free from any constraints on the ground.For helping identifying the behaviors and intention of the flying objects,the recognition of the type of the aircraft tra-jectories has important value.Specifically,on the basis of identifying frequent route patterns,the paper proposes a new method,consisting of a frequent route patterns extracting algorithm and a convolution neural network model.The extracting algorithm first gets key points from the compressed trajectory,next finds the closed routes through the self-intersecting points of the trajectory,then discovers frequent patterns in the closed routes and treats them as the basis of classification.Further,the model recognizes the trajectory type via image analyses.This paper conducts extensive experiments on the real aircraft trajectory data disclosed on the FlightRadar24 website as well as the simulated data.The experimental results show that our method can effectively identify complex trajectory types.Compared with LeNet-5 CNN classification without trajectory extraction,our method has the superior performance,achieving an average accuracy of more than 95% for trajectory classification.

Key words: Aircraft trajectory, Frequent route patterns, Pattern mining, Trajectory classification, Trajectory pattern mining