PROGRESS IN GEOGRAPHY ›› 2011, Vol. 30 ›› Issue (5): 522-534.doi: 10.11820/dlkxjz.2011.05.002

• Original Articles • Previous Articles     Next Articles

Review of the Research Progresses in Trajectory Clustering Methods

GONG Xi1,2, PEI Tao1, SUN Jia2,3, LUO Ming4   

  1. 1. State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China;
    2. Yantai Institute of Coastal Zone Research, CAS, Yantai 264003, Shandong,China;
    3. Graduate University of Chinese Academy of Sciences, Beijing 100049, China;
    4. Department of Geography and Resource Management, The Chinese University of Hong Kong, Hong Kong, China
  • Received:2010-10-01 Revised:2011-02-01 Online:2011-05-25 Published:2011-05-25

Abstract: A trajectory is a sequence of the location and timestamp of a moving object. It is not only an important type of spatio-temporal data, but also a critical source of information. Extracting patterns from different trajectory data can help people understand the drives and outcomes of individual and collective spatial dynamics, such as human behavior patterns, transport and logistics, emergency evacuation management, animal behavior, and marketing. Recently, a larger number of trajectory data are available for analyzing the temporal and spatial pattern, as the result of the improvements of tracking facilities and sensor networks. Therefore, clustering analysis needs to be used to find the implicit patterns in it. Based on the characteristics and the similarity measurements of trajectory data, this paper reviewed the research progresses in trajectory clustering methods. Firstly, the significance of research on trajectory data and its clustering methods was presented. Then the definition, models as well as several visualization methods of trajectories were summarized. After that, the authors classified the existing trajectory clustering methods into 6 main categories according to the similarity measurement of them, and analyzed each of the trajectory clustering methods, along with their respective pros and cons by category. Finally, some research challenges and future directions were discussed.

Key words: clustering, research progress, similarity measurement, spatio-temporal data mining, trajectory