UTSM: A Trajectory Similarity Measure Considering Uncertainty Based on an Amended Ellipse Model

Abstract

Measuring the similarity between a pair of trajectories is the basis of many spatiotemporal clustering methods and has wide applications in trajectory pattern mining. However, most measures of trajectory similarity in the literature are based on precise models that ignore the inherent uncertainty in trajectory data recorded by sensors. Traditional computing or mining approaches that assume the preciseness and exactness of trajectories therefore risk underperforming or returning incorrect results. To address the problem, we propose an amended ellipse model, which takes both interpolation error and positioning error into account by making use of the motion features of the trajectory to compute the ellipse’s shape parameters. A specialized similarity measure method considering uncertainty called the Uncertain Trajectory Similarity Measure (UTSM) based on the model is also proposed. We validate the approach experimentally on both synthetic and real-world data and show that UTSM is not only more robust to noise and outliers, but also more tolerant of different sample frequencies and asynchronous sampling of trajectories.