Fast Movelet Extraction and Dimensionality Reduction for Robust Multiple Aspect Trajectory Classification

Abstract

Mobility data analysis has received significant attention in the last few years. Enriching spatial-temporal trajectory data with semantic information, which is the definition of Multiple Aspect Trajectories, presents lots of opportunities, but also many challenges. Regarding trajectory classification, the state-of-the-art method called MASTERMovelets has shown to have the best classification accuracy over several datasets. Indeed, this method generates interpretable patterns called movelets which are the most discriminant sequences of points. Despite its increased performance, the method is computationally expensive and does not scale well, which makes its application unfeasible for large datasets. In this paper we propose a pivot based approach to reduce the search space, selecting only most promising trajectory points to extract movelets. We additionally provide a method to define a limited number of semantic dimensions for movelets. Experiments show that the proposed method is at least $50\%$ faster for extracting the movelets, and shows a average drop of $82\%$ of input to the classification models while keeping a similar classification accuracy level. Additionally, our scalability analysis with respect to computation time shows that the proposed method scales better than the other methods as the dataset grows in number of points, trajectories and dimensions.