A Trajectory Similarity Calculation Framework for Multiple Scenarios
Trajectory similarity calculation, as a research hotspot in trajectory data analysis, is mainly used to measure the degree of similarity between the trajectories of two or more moving objects. The existing trajectory similarity calculation work mainly proposes a large number of trajectory similarity measurement methods based on different types of trajectory data and application scenarios. However, with the enrichment of trajectory data collection methods and the development of application requirements, there is a lack of a unified way to supporttrajectory similarity calculation for multiple scenarios. Therefore, based on a comprehensive analysis of the characteristics of existing similar trajectory measurement methods, this article proposes an integrated calculation framework, named as Multiple Scenarios Similarity Trajectory Framework ( MSSTJ) that is compatible with multiple trajectory similarity measurement methods, and optimizes the computational efficiency of different similar trajectory measurement methods through a unified trajectory data partitioning index method. This enables users to achieve efficient calculation of diverse trajectory similarity in different scenarios through a simple parameterized configuration way. Experiments based on two different types of trajectory datasets, show that MSSTJ can help users quickly implement different trajectory similarity calculationalgorithms and applications with good performance.
trajectory similarity calculationmultiple scenariosintegrated calculation frameworkpartition index optimizationtrajectory similarity algorithm
万方数据
维普
