Research on The Effectiveness of Trajectory Similarity Measurement Algorithm

Abstract

Under the background of big data era, massive trajectory data is constantly generated, which contains great value. The analysis and mining of spatiotemporal trajectory data is a research hotspot of spatial data, including trajectory retrieval, trajectory classification etc. In the process of analysis and mining, the similarity measurement between different trajectories is a key problem. Considering the amount of data, computational complexity, noise and other factors, different metrics need to be selected in different situations. In order to understand the performance of different similarity measurement algorithms, this paper selects the Longest Common Sub-Sequence Algorithm (LCSS), Fréchet Distance Algorithm and One Way Distance Algorithm (OWD) among the three kinds of trajectory similarity measurement algorithms and they were used for comparison. Experimental results show that LCSS algorithm can effectively resist the interference of noise points, Fréchet Distance Algorithm has strong robustness, and OWD Algorithm has low time complexity and short execution time.