Mining Spatio-Temporal Semantic Trajectory for Groups Identification

Abstract

Group identification refers to discovering groups with similar behaviors or preferences. The daily trajectories record the activities of moving objects, which reflect their behaviors. These mobile data provide us with a new data analysis approach for groups identification. Therefore, we can identify users or groups based on similar trajectory patterns by mining implicit behaviors patterns. In this paper, we propose a novel trajectory semantics calculation method to identify groups that have similar behaviors. In our model, we first propose a fast and efficient two-phase discovering stay regions method (TPD) from daily trajectories, then generate semantic trajectories by enriching the stay regions with semantic labels based on POI information and LDA topic model. To measure the similarity between semantic trajectories, we design a semantic similarity measure model based on geographic and semantic similarity factor. We have conducted extensive experiments on real trajectory dataset of Geolife project, and the experimental results show our proposed method is both effective and efficient.