Trajectory pattern mining

TLDR

The widespread use of location‑acquisition technologies is generating large spatio‑temporal datasets that enable the discovery of movement‑behaviour patterns for new applications. This work extends sequential pattern mining to analyze moving‑object trajectories. We define trajectory patterns as concise space‑and‑time descriptions of frequent behaviours, formalize the mining problem, explore several instantiations, and evaluate them on real and synthetic data.

Abstract

The increasing pervasiveness of location-acquisition technologies (GPS, GSM networks, etc.) is leading to the collection of large spatio-temporal datasets and to the opportunity of discovering usable knowledge about movement behaviour, which fosters novel applications and services. In this paper, we move towards this direction and develop an extension of the sequential pattern mining paradigm that analyzes the trajectories of moving objects. We introduce trajectory patterns as concise descriptions of frequent behaviours, in terms of both space (i.e., the regions of space visited during movements) and time (i.e., the duration of movements). In this setting, we provide a general formal statement of the novel mining problem and then study several different instantiations of different complexity. The various approaches are then empirically evaluated over real data and synthetic benchmarks, comparing their strengths and weaknesses.