Approximate Similarity Measurements on Multi-Attributes Trajectories Data

Abstract

With the development of global positioning technology, sensor networks, and smart mobile terminal, a large number of trajectory data are accumulated. Trajectory data contains a wealth of information, including spatiality, time series, and other external descriptive attributes (i.e., features, travelling mode, and so on). Trajectory analysis and mining show the great value. The research of trajectory similarity measurement is the basis of trajectory data management and mining, which plays an important role in trajectory computing. Most trajectory similarity work only focuses on the spatial-temporal features. The addition of multi-attributes to the trajectories changes the trajectory similarity. However, there are few researches focusing on multi-attributes trajectory similarity. In this paper, we propose two novel trajectory similarity measurements, i.e. maximum-minimum trajectory distance and sum of minimum trajectory distance and analyze the correlation among the spatial-temporal similarity and textual similarity. Finally, the measurement validity is verified and visualized through clustering, by both a simulation dataset and a real dataset.

Keywords

• Trajectory similarity measurement
• clustering
• big trajectory data
• trajectory computing