Key techniques for predicting the uncertain trajectories of moving objects with dynamic environment awareness

Abstract

Emerging technologies of wireless and mobile communication enable people to accumulate a large volume of time-stamped locations, which appear in the form of a continuous moving object trajectory. How to accurately predict the uncertain mobility of objects becomes an important and challenging problem. Existing algorithms for trajectory prediction in moving objects databases mainly focus on identifying frequent trajectory patterns, and do not take account of the effect of essential dynamic environmental factors. In this study, a general schema for predicting uncertain trajectories of moving objects with dynamic environment awareness is presented, and the key techniques in trajectory prediction are addressed in detail. In order to accurately predict the trajectories, a trajectory prediction algorithm based on continuous time Bayesian networks (CTBNs) is improved and applied, which takes dynamic environmental factors into full consideration. Experiments conducted on synthetic trajectory data verify the effectiveness of the improved algorithm, which also guarantees the time performance as well.