Vector Field Model for Trajectory Data and Its Application in Similarity Query

Yiling Jia, Che-Rung Lee

Abstract: With the increasing number of mobile global positioning system (GPS) devices, more and more trajectory data are collected, stored, and analyzed for various applications. One of the basic operations in trajectory analysis is the similarity query, which retrieves similar trajectories of a given one. In this paper, we model trajectory data as vector fields, by which the similarity between two trajectories can be measured in the vector space that they are transformed to. Using such model, trajectory queries can be performed efficiently using Locality Sensitive Hashing (LSH) to filter out the dissimilar trajectories. Experiments using Geolife dataset demonstrate that LSH can filter out nearly 70% dissimilar trajectories while maintaining the recall rate close to 100%. Meanwhile, experiments show that our method is 30 times faster than the tradition Longest Common Subsequence (LCSS) method when querying ten thousands of trajectory data.