Mining moving patterns for predicting next location

Next location prediction has been an essential task for many location based applications such as targeted advertising. In this paper, we present three basic models to tackle the problem of predicting next locations: the Global Markov Model that uses all available trajectories to discover global behaviors, the Personal Markov Model that focuses on mining the individual patterns of each moving object, and the Regional Markov Model that clusters the trajectories to mine the similar movement patterns. The three models are integrated with linear regression in different ways. We then seek to further improve the accuracy of prediction by considering the time factor, with a focus on clustering the trajectories in different time periods, and present three methods to train the time-aware models to mine periodic patterns. Therefore, our proposed models have the following advantages: (1) we consider both individual and collective movement patterns in making prediction, (2) we consider the similarity between different trajectories, (3) we consider the time factor and build models that are suited to different time periods. We have conducted extensive experiments on a real dataset, and the results demonstrate the superiority of our proposed models over existing methods. (C) 2015 Elsevier Ltd. All rights reserved.