, Volume 21, Issue 4, pp 669–701 | Cite as

Snapshot and continuous points-based trajectory search

  • Shuyao QiEmail author
  • Dimitris Sacharidis
  • Panagiotis Bouros
  • Nikos Mamoulis


Trajectory data capture the traveling history of moving objects such as people or vehicles. With the proliferation of GPS and tracking technologies, huge volumes of trajectories are rapidly generated and collected. Under this, applications such as route recommendation and traveling behavior mining call for efficient trajectory retrieval. In this paper, we first focus on distance-to-points trajectory search; given a collection of trajectories and a set query points, the goal is to retrieve the top-k trajectories that pass as close as possible to all query points. We advance the state-of-the-art by combining existing approaches to a hybrid nearest neighbor-based method while also proposing an alternative, more efficient spatial range-based approach. Second, we investigate the continuous counterpart of distance-to-points trajectory search where the query is long-standing and the set of returned trajectories needs to be maintained whenever updates occur to the query and/or the data. Third, we propose and study two practical variants of distance-to-points trajectory search, which take into account the temporal characteristics of the searched trajectories. Through an extensive experimental analysis with real trajectory data, we show that our range-based approach outperforms previous methods by at least one order of magnitude for the snapshot and up to several times for the continuous version of the queries.


Trajectory search Continuous queries Spatial proximity 



Work supported by grant 17205015 from Hong Kong RGC.


  1. 1.
    Böhm C, Berchtold S, Keim DA (2001) Searching in high-dimensional spaces: index structures for improving the performance of multimedia databases. ACM Comput Surv 33(3):322–373CrossRefGoogle Scholar
  2. 2.
    Chen Z, Shen HT, Zhou X, Zheng Y, Xie X (2010) Searching trajectories by locations: an efficiency study. In: SIGMOD, pp 255–266Google Scholar
  3. 3.
    Fagin R, Lotem A, Naor M (2001) Optimal aggregation algorithms for middleware. In: PODS, pp 102–113Google Scholar
  4. 4.
    Frentzos E, Gratsias K, Pelekis N, Theodoridis Y (2007) Algorithms for nearest neighbor search on moving object trajectories. GeoInformatica 11(2):159–193CrossRefGoogle Scholar
  5. 5.
    Güntzer U, Balke W, Kießling W (2001) Towards efficient multi-feature queries in heterogeneous environments. In: ITCC, pp 622–628Google Scholar
  6. 6.
    Güntzer U, Balke WT, Kießling W (2000) Optimizing multi-feature queries for image databases. In: VLDB, pp 419–428Google Scholar
  7. 7.
    Hjaltason GR, Samet H (1999) Distance browsing in spatial databases. ACM Trans Database Syst 24(2):265–318CrossRefGoogle Scholar
  8. 8.
    Ilyas IF, Beskales G, Soliman MA (2008) A survey of top-k query processing techniques in relational database systems. ACM Comput Surv 40(4)Google Scholar
  9. 9.
    Jagadish HV, Ooi BC, Tan K, Yu C, Zhang R (2005) iDistance: an adaptive b +-tree based indexing method for nearest neighbor search. ACM Trans Database Syst 30 (2):364–397CrossRefGoogle Scholar
  10. 10.
    Lee JG, Han J, Whang KY (2007) Trajectory clustering: a partition-and-group framework. In: SIGMOD, pp 593–604Google Scholar
  11. 11.
    Li X, Han J, Lee JG, Gonzalez H (2007) Traffic density-based discovery of hot routes in road networks. In: SSTD, pp 441–459Google Scholar
  12. 12.
    Mouratidis K, Hadjieleftheriou M, Papadias D (2005) Conceptual partitioning: an efficient method for continuous nearest neighbor monitoring. In: SIGMOD, pp 634–645Google Scholar
  13. 13.
    Papadias D, Tao Y, Mouratidis K, Hui CK (2005) Aggregate nearest neighbor queries in spatial databases. ACM Trans Database Syst 30(2):529–576CrossRefGoogle Scholar
  14. 14.
    Pfoser D, Jensen CS, Theodoridis Y (2000) Novel approaches to the indexing of moving object trajectories. In: VLDB, pp 395–406Google Scholar
  15. 15.
    Qi S, Bouros P, Sacharidis D, Mamoulis N (2015) Efficient point-based trajectory search. In: SSTD, pp 179–196Google Scholar
  16. 16.
    Roussopoulos N, Kelley S, Vincent F (1995) Nearest neighbor queries. In: SIGMOD, pp 71–79Google Scholar
  17. 17.
    Song Z, Roussopoulos N (2001) K-nearest neighbor search for moving query point. In: SSTD, pp 79–96Google Scholar
  18. 18.
    Tang LA, Zheng Y, Xie X, Yuan J, Yu X, Han J (2011) Retrieving k-nearest neighboring trajectories by a set of point locations. In: SSTD, pp 223–241Google Scholar
  19. 19.
    Tao Y, Yi K, Sheng C, Kalnis P (2009) Quality and efficiency in high dimensional nearest neighbor search. In: SIGMOD, pp 563–576Google Scholar
  20. 20.
    Xiong X, Mokbel MF, Aref WG (2005) Sea-cnn: scalable processing of continuous k-nearest neighbor queries in spatio-temporal databases. In: ICDE, pp 643–654Google Scholar
  21. 21.
    Yu X, Pu KQ, Koudas N (2005) Monitoring k-nearest neighbor queries over moving objects. In: ICDE, pp 631–642Google Scholar
  22. 22.
    Zhang J, Zhu M, Papadias D, Tao Y, Lee DL (2003) Location-based spatial queries. In: SIGMOD, pp 443–454Google Scholar
  23. 23.
    Zheng Y (2015) Trajectory data mining: an overview. ACM Trans Intell Syst Tech 6(3):29:1–29:41CrossRefGoogle Scholar
  24. 24.
    Zheng Y, Li Q, Chen Y, Xie X, Ma W (2008) Understanding mobility based on GPS data. In: Ubicomp, pp 312–321Google Scholar
  25. 25.
    Zheng Y, Xie X, Ma W (2010) Geolife: a collaborative social networking service among user, location and trajectory. IEEE Data Eng Bull 33(2):32–39Google Scholar
  26. 26.
    Zheng Y, Zhang L, Xie X, Ma WY (2009) Mining interesting locations and travel sequences from gps trajectories. In: WWW, pp 791–800Google Scholar
  27. 27.
    Zheng Y, Zhou X (eds) (2011) Computing with spatial trajectories. SpringerGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  • Shuyao Qi
    • 1
    Email author
  • Dimitris Sacharidis
    • 2
  • Panagiotis Bouros
    • 3
  • Nikos Mamoulis
    • 1
  1. 1.Department of Computer ScienceThe University of Hong KongHong KongChina
  2. 2.Faculty of InformaticsTechnische Universität WienViennaAustria
  3. 3.Department of Computer ScienceAarhus UniversityAarhusDenmark

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