Four dimensional (4D) flight trajectories play an important role in air traffic future plans. In this paper, the time and altitude variables in 4D trajectories are analyzed for their characteristics, and the procedure of preprocessing flight trajectory data is provided, and support vector regression and decision tree regression are introduced to build the prediction models for trajectory time and altitude, respectively. It is demonstrated by the experiments on actual flight trajectory data that the proposed method can improve the 4D trajectory prediction accuracy effectively.
The measurement of trajectory distance is the base of trajectory clustering. To deal with the flight trajectory clustering in air traffic, a novel method is proposed in this paper to measure the flight trajectory distance. This method views the trajectory as a set of segments, whose end points are trajectory points, and it measures the distance from a trajectory point to another trajectory, and thus presents the distance definition of trajectories. Based on the calculated distance matrix, spectral clustering algorithm is adopted to cluster flight trajectories. The experiment on actual flight trajectory data verifies the effectiveness of the proposed method.