Effective spatio-temporal semantic trajectory generation for similar pattern group identification

The daily trajectories of individual movements convey a concise overview of their behaviors, with different social roles having different trajectory patterns. Therefore, we can identify users or groups based on the similar of their trajectory patterns. However, most existing trajectory analysis focuses only on the spatial and temporal analyses of the raw trajectory data and misses essential semantic information concerning behaviors. In this paper, we propose a new trajectory semantics calculation method to identify groups with similar behaviors. We first propose a fast and efficient two-phase method for identifying stay regions within daily trajectories and enriching the stay regions with semantic labels based on points of interest to generate semantic trajectories. Furthermore, we design a semantic similarity measure model using geographic and semantic similarity factors to measure the similarity between semantic trajectories. We also propose a pruning strategy using time entropy to decrease the number of complex calculations and comparisons to improve performance. The results of our extensive experiments on the real trajectory dataset of the Geolife project show that our proposed method is both effective and efficient.