Point-of-Interest Demand Discovery Using Semantic Trajectories

Semantic trajectories have become unprecedentedly available because of the rapidly growing popularities of location-sharing services. People’s lifestyles and Point-of-Interest demands are hidden in such data. Extracting people’s POI needs for different regions from semantic trajectories plays an important role in site selection, which can be widely used in city planning, facility location and other applications. However, most of existing works either use traditional trajectories which need to infer semantic with external information and lead to inaccuracy, or just focus on specific category. Semantic trajectory mining provides us a new way to address the challenges. Based on above motivation, we study the regional POI demand discovery problem using semantic trajectories. In this paper, we carefully analyze the features of semantic trajectory data and people’ mobility patterns. Then, we propose an effective POI demand modeling method. Furthermore, we propose two efficient algorithms to identify the regional POI demands. The proposed algorithms extract regional patterns and compute the regional POIs demand according to POI demand model. Finally, the ranked POIs demands for regions are obtained. We evaluate the proposed modeling method and algorithms in terms of efficiency and effectiveness on two real data sets. The results show that our proposed methods outperform the competitor for both efficiency and effectiveness.