Exploring spatial-temporal trajectory model for location prediction

Location prediction has attracted a significant amount of research effort. Given an object's recent movements and a future time, the goal of location prediction is to predict the location of this object at the future time specified. Prior works have elaborated on mining association relationships among regions, in which objects frequently appear, to predict locations. Association relationships among regions are represented as association rules. By exploring association relationships among regions, prior works are able to have a good accuracy for location prediction. However, with a large amount of trajectory data produced, a huge amount of association rules is expected. Furthermore, trajectory data has both the spatial and temporal information. To further enhance the accuracy of location prediction, one could utilize not only spatial information but also temporal information to estimate locations of objects. In this paper, we propose a spatial-temporal trajectory model (abbreviated as STT) to capture movement behaviors of objects. STT is represented as a probabilistic suffix tree with both spatial and temporal information of movements. Note that STT is able to discover sequential traversal relationships among regions and, for each region, STT derives the corresponding probabilities about the time when objects appear. With the nature of probabilistic suffix tree, we could use a compact structure to capture movement behavior of objects compared to association rules proposed. In light of STT, we further propose an algorithm to traverse STT for location prediction. By exploring both the spatial and temporal information of STT, the accuracy of location prediction is improved. To evaluate our proposed STT and prediction algorithm, we conduct experiments on the synthetic dataset generated from real datasets. Experimental results shows that our proposed STT is able to capture both spatial and temporal patterns of movement behaviors and, by exploring STT, our proposed prediction algorithm outperforms existing works.