A Multiple-motion-pattern Trajectory Prediction Model for Uncertain Moving Objects

This study aims to solve the problem of predicting uncertain trajectories of moving objects, including mobile devices, vehicles, airplanes, and hurricanes. In order to design a general schema of trajectory prediction on large-scale moving objects data, techniques of frequent trajectory patterns mining and Gaussian mixture regression model are employed, and a multiple-motion-pattern trajectory prediction model is proposed. The proposed key techniques include: 1) as for simple motion patterns, a new trajectory prediction algorithm based on frequent trajectory pattern tree (FTP-tree) is proposed, which employs a density based region-of-interest discovery approach to partition a large number of trajectory points into distinct clusters. Then, it generates a frequent trajectory pattern tree to forecast continuous locations of moving objects. Experimental results show that the FTP-tree based trajectory prediction algorithm performs better than existing prediction approaches with the guarantee of time efficiency. 2) Gaussian mixture regression approach is used to model complex multiple motion patterns, which calculates the probability distribution of different types of motion patterns, as well as partitions trajectory data into distinct components, in order to predict the most possible trajectories of moving objects via Gaussian process regression. Experimental results show a high accuracy and low time consumption on trajectory prediction, as compared to the hidden Markov model approach and the Kalman filter one.