Towards Semantic-Aware Multiple-Aspect Trajectory Similarity Measuring

The large amount of semantically rich mobility data becoming avail- able in the era of Big Data, has led to the need for new trajectory similarity measures. In the context of multiple-aspect trajectories, where mobility data are enriched with several semantic dimensions, current state-of-the-art approaches present some limitations concerning the relationships between attributes and their semantics. Existing works are too strict requiring a match on all attributes, or too flexible, considering all attributes as independent. In this paper we pro- pose MUITAS, a novel similarity measure for a new type of trajectory data with heterogeneous semantic dimensions, which takes into account the semantic re- lationship between attributes, thus filling the gap of the current trajectory simi- larity methods. We evaluate MUITAS over two real datasets of multiple aspect social media and GPS trajectories. With precision at recall and clustering tech- niques, we show that MUITAS is the most robust measure for multiple-aspect trajectories.