Moving Object Trajectory Based Spatio-Temporal Mobility Prediction.

Mobility prediction for individual trajectory is a challenging topic. The aim of the study is to develop a simple but effective method to predict when the user will leave from the current location and where he will move next. The proposed method performs the predictions in three continuous, sequential phases. In the first phase, the method continuously extracts grid-based stay-time statistics from the GPS coordinate stream of the location-aware mobile device of the user. In the second phase, from the grid-based stay-time statistics, the method periodically extracts and manages regions that the user frequently visits. Finally, in the third phase, from the stream of region-visits, the method continuously estimates the parameters of an inhomogeneous continuous-time Markov model and in a continuous fashion predicts when the user will leave his current region and where he will move next. The temporal and spatial prediction accuracy of the method has been evaluated using a single long trajectory from the GeoLife data set. The results show that for the optimal method parameter settings, the method can predict the departure time on average to be within 83 minutes of the actual departure time and can predict the next region correctly in 51% of the cases.