Journal of Computer Science and Technology

, Volume 33, Issue 4, pp 792–806 | Cite as

Complete Your Mobility: Linking Trajectories Across Heterogeneous Mobility Data Sources

  • Guo-Wei WangEmail author
  • Jin-Dou Zhang
  • Jing Li
Regular Paper


Nowadays, human activities and movements are recorded by a variety of tools, forming different trajectory sets which are usually isolated from one another. Thus, it is very important to link different trajectories of one person in different sets to provide massive information for facilitating trajectory mining tasks. Most prior work took advantages of only one dimensional information to link trajectories and can link trajectories in a one-to-many manner (providing several candidate trajectories to link to one specific trajectory). In this paper, we propose a novel approach called one-to-one constraint trajectory linking with multi-dimensional information (OCTL) that links the corresponding trajectories of one person in different sets in a one-to-one manner. We extract multidimensional features from different trajectory datasets for corresponding relationships prediction, including spatial, temporal and spatio-temporal information, which jointly describe the relationships between trajectories. Using these features, we calculate the corresponding probabilities between trajectories in different datasets. Then, we formulate the link inference problem as a bipartite graph matching problem and employ effective methods to link one trajectory to another. Moreover, the advantages of our approach are empirically verified on two real-world trajectory sets with convincing results.


trajectory linking trajectory data mining trajectory similarity mobility pattern mining 


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Supplementary material

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  1. [1]
    Zheng Y. Trajectory data mining: An overview. ACM Trans. Intelligent Systems and Technology (TIST), 2015, 6(3): Article No. 29.Google Scholar
  2. [2]
    Wang Y Z, Yuan N J, Lian D F, Xu L L, Xie X, Chen E H, Rui Y. Regularity and conformity: Location prediction using heterogeneous mobility data. In Proc. the 21st ACM SIGKDD Int. Conf. Knowledge Discovery and Data Mining, August 2015, pp.1275-1284.Google Scholar
  3. [3]
    Zheng Y, Zhang L Z, Ma Z X, Xie X, Ma W Y. Recommending friends and locations based on individual location history. ACM Trans. the Web (TWEB), 2011, 5(1): Article No. 5.Google Scholar
  4. [4]
    Xiao X Y, Zheng Y, Luo Q, Xie X. Inferring social ties between users with human location history. Journal of Ambient Intelligence and Humanized Computing, 2014, 5(1):3-19.CrossRefGoogle Scholar
  5. [5]
    Zheng Y, Capra L, Wolfson O, Yang H. Urban computing: Concepts, methodologies, and applications. ACM Trans. Intelligent Systems and Technology (TIST) 2014, 5(3): Article No. 38.Google Scholar
  6. [6]
    Esling P, Agon C. Time-series data mining. ACM Computing Surveys (CSUR), 2012, 45(1): Article No. 12.Google Scholar
  7. [7]
    Cao W, Wu Z W, Wang D, Li J, Wu H S. Automatic user identification method across heterogeneous mobility data sources. In Proc. the 32nd Int. Conf. Data Engineering, May 2016, pp.978-989.Google Scholar
  8. [8]
    Wu H Y, Xue M Q, Cao J N, Karras P, Ng W S, Koo K K. Fuzzy trajectory linking. In Proc. the 32nd Int. Conf. Data Engineering, May 2016, pp.859-870.Google Scholar
  9. [9]
    Li Q N, Zheng Y, Xie X, Chen Y K, Liu W Y, Ma W Y. Mining user similarity based on location history. In Proc. the 16th ACM SIGSPATIAL Int. Conf. Advances in Geographic Information Systems, November 2008.Google Scholar
  10. [10]
    Jones K S. A statistical interpretation of term specificity and its application in retrieval. Journal of Documentation, 1972, 28(1): 11-21.CrossRefGoogle Scholar
  11. [11]
    Das G, Gunopulos D, Mannila H. Finding similar time series. In Proc. the 1st European Symp. Principles of Data Mining and Knowledge Discovery, June 1997, pp.88-100.Google Scholar
  12. [12]
    Cortes C, Vapnik V. Support-vector networks. Machine Learning, 1995, 20(3): 273-297.zbMATHGoogle Scholar
  13. [13]
    Walker S H, Duncan D B. Estimation of the probability of an event as a function of several independent variables. Biometrika, 1967, 54(1/2): 167-179.Google Scholar
  14. [14]
    Chawla N V. Data mining for imbalanced datasets: An overview. In Data Mining and Knowledge Discovery Handbook, Maimon O, Rokach L (eds.), Springer, 2009, pp.875-886.Google Scholar
  15. [15]
    Chai X Y, Deng L, Yang Q, Ling C X. Test-cost sensitive naive Bayes classification. In Proc. the 4th IEEE Int. Conf. Data Mining, November 2004, pp.51-58.Google Scholar
  16. [16]
    Dietterich T G. Ensemble methods in machine learning. In Proc. the 1st Int. Workshop on Multiple Classifier Systems, June 2000.Google Scholar
  17. [17]
    Chawla N V, Bowyer K W, Hall L O, Kegelmeyer W P. SMOTE: Synthetic minority over-sampling technique. Journal of Artificial Intelligence Research, 2002, 16(1): 321-357.CrossRefzbMATHGoogle Scholar
  18. [18]
    Raskutti B, Kowalczyk A. Extreme re-balancing for SVMs: A case study. ACM SIGKDD Explorations Newsletter, 2004, 6(1): 60-69.Google Scholar
  19. [19]
    Ho T K. Random decision forests. In Proc. the 3rd Int. Conf. Document Analysis and Recognition, August 1995, pp.278-282.Google Scholar
  20. [20]
    Munkres J. Algorithms for the assignment and transportation problems. Journal of the Society for Industrial and Applied Mathematics, 1957, 5(1): 32-38.MathSciNetCrossRefzbMATHGoogle Scholar
  21. [21]
    Kong X N, Zhang JW, Yu P S. Inferring anchor links across multiple heterogeneous social networks. In Proc. the 22nd ACM Int. Conf. Information & Knowledge Management, October 2013, pp.179-188.Google Scholar
  22. [22]
    Agrawal R, Faloutsos C, Swami A. Efficient similarity search in sequence databases. In Proc. the 4th Int. Conf. Foundations of Data Organization and Algorithms, October 1993, pp.69-84.Google Scholar
  23. [23]
    Chen L, Ng R. On the marriage of Lp-norms and edit distance. In Proc. the 30th Int. Conf. Very Large Data Bases-Volume 30, August 2004, pp.792-803.Google Scholar
  24. [24]
    Nanni M, Pedreschi D. Time-focused clustering of trajectories of moving objects. Journal of Intelligent Information Systems, 2006, 27(3): 267-289.CrossRefGoogle Scholar
  25. [25]
    Chen L, Özsu M T, Oria V. Robust and fast similarity search for moving object trajectories. In Proc. ACM SIGMOD Int. Conf. Management of Data, June 2005, pp.491-502.Google Scholar
  26. [26]
    Pelekis N, Kopanakis I, Marketos G, Ntoutsi I, Andrienko G, Theodoridis Y. Similarity search in trajectory databases. In Proc. the 14th Int. Symp. Temporal Representation and Reasoning, June 2007, pp.129-140.Google Scholar
  27. [27]
    Wang X Y, Mueen A, Ding H, Trajcevski G, Trajcevski P, Keogh E. Experimental comparison of representation methods and distance measures for time series data. Data Mining and Knowledge Discovery, 2013, 26(2): 275-309.MathSciNetCrossRefGoogle Scholar

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© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Computer Science and TechnologyUniversity of Science and Technology of ChinaHefeiChina

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