4.6 Article

A Survey on Trajectory Data Mining: Techniques and Applications

Journal

IEEE ACCESS
Volume 4, Issue -, Pages 2056-2067

Publisher

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2016.2553681

Keywords

Trajectory data mining; big data applications; data mining techniques

Funding

  1. 973 Program [2014CB340303]
  2. National Natural Science Foundation of China [61472254, 61170238, 61420106010]
  3. Science and Technology Commission of Shanghai [14511107500]
  4. National Research Foundation Singapore Energy and Environmental Sustainability Solutions for Megacities under the Campus for Research Excellence and Technological Enterprise framework
  5. Program for New Century Excellent Talents in University
  6. Program for Changjiang Scholars and Innovative Research Team in University (Young Scholars), China

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Rapid advance of location acquisition technologies boosts the generation of trajectory data, which track the traces of moving objects. A trajectory is typically represented by a sequence of timestamped geographical locations. A wide spectrum of applications can benefit from the trajectory data mining. Bringing unprecedented opportunities, large-scale trajectory data also pose great challenges. In this paper, we survey various applications of trajectory data mining, e.g., path discovery, location prediction, movement behavior analysis, and so on. Furthermore, this paper reviews an extensive collection of existing trajectory data mining techniques and discusses them in a framework of trajectory data mining. This framework and the survey can be used as a guideline for designing future trajectory data mining solutions.

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