A survey of trajectory distance measures and performance evaluation

  • Han Su
  • Shuncheng Liu
  • Bolong Zheng
  • Xiaofang Zhou
  • Kai ZhengEmail author
Special Issue Paper


The proliferation of trajectory data in various application domains has inspired tremendous research efforts to analyze large-scale trajectory data from a variety of aspects. A fundamental ingredient of these trajectory analysis tasks and applications is distance measures for effectively determining how similar two trajectories are. We conduct a comprehensive survey of the trajectory distance measures. The trajectory distance measures are classified into four categories according to the trajectory data type and whether the temporal information is measured. In addition, the effectiveness and complexity of each distance measure are studied. The experimental study is also conducted on their effectiveness in the six different trajectory transformations.


Trajectory distance measure Trajectory transformation Objective evaluation 



This research is supported by the NSFC (Grant Nos. 618020 54, 61972069, 61836007, 61832017, 61532018, 61902134), the Central Universities (UESTC: Grants No. ZYGX2016K YQD135, HUST: Grants Nos. 2019kfyXKJC021, 2019kfyX JJS091), and Dongguan Innovative Research Team Program (No. 2018607201008).


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Authors and Affiliations

  1. 1.University of Electronic Science and Technology of ChinaChengduChina
  2. 2.Huazhong University of Science and TechnologyWuhanChina
  3. 3.University of QueenslandBrisbaneAustralia
  4. 4.Institute of Electronic and Information Engineering of UESTC in GuangdongDongguanChina

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