An effectiveness study on trajectory similarity measures

  • Authors:

  • Haozhou Wang;Han Su;Kai Zheng;Shazia Sadiq;Xiaofang Zhou

  • Affiliations:

  • The University of Queensland, Brisbane, QLD, Australia;The University of Queensland, Brisbane, QLD, Australia;The University of Queensland, Brisbane, QLD, Australia;The University of Queensland, Brisbane, QLD, Australia;The University of Queensland, Brisbane, QLD, Australia

  • Venue:
  • ADC '13 Proceedings of the Twenty-Fourth Australasian Database Conference - Volume 137
  • Year:
  • 2013

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Abstract

The last decade has witnessed the prevalence of sensor and GPS technologies that produce a sheer volume of trajectory data representing the motion history of moving objects. Measuring similarity between trajectories is undoubtedly one of the most important tasks in trajectory data management since it serves as the foundation of many advanced analyses such as similarity search, clustering, and classification. In this light, tremendous efforts have been spent on this topic, which results in a large number of trajectory similarity measures. Generally, each individual work introducing a new distance measure has made specific claims on the superiority of their proposal. However, for most works, the experimental study was focused on demonstrating the efficiency of the search algorithms, leaving the effectiveness aspect unverified empirically. In this paper, we conduct a comparative experimental study on the effectiveness of six widely used trajectory similarity measures based on a real taxi trajectory dataset. By applying a variety of transformations we designed for each original trajectory, our experimental observations demonstrate the advantages and drawbacks of these similarity measures in different circumstances.