Towards semantic-aware multiple-aspect trajectory similarity measuring

The large amount of semantically rich mobility data becoming available in the era of big data has led to a 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 either too strict, requiring a match on all attributes, or too flexible, considering all attributes as independent. In this article we propose MUITAS, a novel similarity measure for a new type of trajectory data with heterogeneous semantic dimensions, which takes into account the semantic relationship between attributes, thus filling the gap of the current trajectory similarity methods. We evaluate MUITAS over two real datasets of multiple-aspect social media and GPS trajectories. With precision at recall and clustering techniques, we show that MUITAS is the most robust measure for multiple-aspect trajectories.