Journal of Computer Science and Technology

, Volume 30, Issue 4, pp 745–761 | Cite as

Trip Oriented Search on Activity Trajectory

  • Wei Chen
  • Lei ZhaoEmail author
  • Jia-Jie Xu
  • Guan-Feng Liu
  • Kai Zheng
  • Xiaofang Zhou
Regular Paper


Driven by the flourish of location-based services, trajectory search has received significant attentions in recent years. Different from existing studies that focus on searching trajectories with spatio-temporal information and text de-scriptions, we study a novel problem of searching trajectories with spatial distance, activities, and rating scores. Given a query q with a threshold of distance, a set of activities, a start point S and a destination E, trip oriented search on activity trajectory (TOSAT) returns k trajectories that can cover the activities with the highest rating scores within the threshold of distance. In addition, we extend the query with an order, i.e., order-sensitive trip oriented search on activity trajectory (OTOSAT), which takes both the order of activities in a query q and the order of trajectories into consideration. It is very challenging to answer TOSAT and OTOSAT efficiently due to the structural complexity of trajectory data with rating information. In order to tackle the problem efficiently, we develop a hybrid index AC-tree to organize trajectories. Moreover, the optimized variant RAC+-tree and novel algorithms are introduced with the goal of achieving higher performance. Extensive experiments based on real trajectory datasets demonstrate that the proposed index structures and algorithms are capable of achieving high efficiency and scalability.


trajectory search rating score activity trajectory 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. [1]
    Li Z, Ding B, Han J, Kays R. Swarm: Mining relaxed temporal moving object clusters. Proceedings of the VLDB Endowment, 2010, 3(1/2): 723-734.CrossRefGoogle Scholar
  2. [2]
    Zheng K, Zheng Y, Yuan N, Shang S, Zhou X. Online discovery of gathering patterns over trajectories. IEEE Trans. Knowledge and Data Engineering, 2014, 26(8): 1974-1988.CrossRefGoogle Scholar
  3. [3]
    Huang M, Hu P, Xia L. A grid based trajectory indexing method for moving objects on fixed network. In Proc. the 18th Int. Conf. Geoinformatics, June 2010.Google Scholar
  4. [4]
    Popa L S, Zeitouni K, Oria V et al. Indexing in-network trajectory flows. The VLDB Journal, 2011, 20(5): 643-669.CrossRefGoogle Scholar
  5. [5]
    Chu S, Yeh C, Huang C. A cloud-based trajectory index scheme. In Proc. the 12th ICEBE, October 2009, pp.602-607.Google Scholar
  6. [6]
    Vlachos M, Kollios G, Gunopulos D. Discovering similar multidimensional trajectories. In Proc. the 18th ICDE, Feb. 26-Mar. 1, 2002, pp.673-684.Google Scholar
  7. [7]
    Chen L, Özsu M T, Oria V. Robust and fast similarity search for moving object trajectories. In Proc. the 24th SIGMOD, June 2005, pp.491-502.Google Scholar
  8. [8]
    Chen Z, Shen H, Zhou X, Zheng Y, Xie X. Searching trajectories by locations: An efficiency study. In Proc. the 29th SIGMOD, June 2010, pp.255-266.Google Scholar
  9. [9]
    Chen Z, Shen H, Zhou X. Discovering popular routes from trajectories. In Proc. the 27th ICDE, April 2011, pp.900-911.Google Scholar
  10. [10]
    Zheng K, Shang S, Yuan N J, Yang Y. Towards efficient search for activity trajectories. In Proc. the 29th ICDE, April 2013, pp.230-241.Google Scholar
  11. [11]
    Zhang C, Han J, Shou L, Lu J, La Porta T. Splitter: Mining fine-grained sequential patterns in semantic trajectories. Proceedings of the VLDB Endowment, 2014, 7(9): 769-780.CrossRefGoogle Scholar
  12. [12]
    Ying J, Lee W, Weng T, Tseng V. Semantic trajectory mining for location prediction. In Proc. the 19th SIGSPATIAL, November 2011, pp.34-43.Google Scholar
  13. [13]
    Zhou Y, Xie X, Wang C, Gong Y, Ma W. Hybrid index structures for location-based web search. In Proc. the 14th International Conference on Information and Knowledge Management, October 31-November 1, 2005, pp.155-162.Google Scholar
  14. [14]
    Chen Y, Suel T, Markowet A. Efficient query processing in geographic web search engines. In Proc. the 25th SIGMOD, June 2006, pp.277-288.Google Scholar
  15. [15]
    Hariharan R, Hore B, Li C, Mehrotra S. Processing spatialkeyword (SK) queries in geographic information retrieval (GIR) systems. In Proc. the 19th SSBDM, July 2007, Article No. 16.Google Scholar
  16. [16]
    Cao X, Cong G, Jensen C, Ooi B. Collective spatial keyword querying. In Proc. the 30th SIGMOD, June 2011, pp.373-384.Google Scholar
  17. [17]
    Shang S, Ding R, Yuan B, Xie K, Zheng K, Kalnis P. User oriented trajectory search for trip recommendation. In Proc. the 15th EDBT, March 2012, pp.156-167.Google Scholar
  18. [18]
    Long C, Wong R, Wang K, Fu A. Collective spatial keyword queries: A distance owner-driven approach. In Proc. the 32nd SIGMOD, June 2013, pp.689-700.Google Scholar
  19. [19]
    Wang C, Xie X, Wang L, Lu Y, Ma W. Web resource geographic location classification and detection. In Proc. the 14th International Conference on World Wide Web, May 2005, pp.1138-1139.Google Scholar
  20. [20]
    De Felipe I, Hristidis V, Rishe N. Keyword search on spatial databases. In Proc. the 24th ICDE, April 2008, pp.656-665.Google Scholar
  21. [21]
    Zhang D, Chee Y, Mondal A, Tung A, Kitsuregawa M. Keyword search in spatial databases: Towards searching by document. In Proc. the 25th ICDE, March 29-April 2, 2009, pp.688-699.Google Scholar
  22. [22]
    Cong G, Jensen C S, Wu D. Efficient retrieval of the top-k most relevant spatial web objects. Proceedings of the VLDB Endowment, 2009, 2(1): 337-348.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  • Wei Chen
    • 1
  • Lei Zhao
    • 1
    • 2
    Email author
  • Jia-Jie Xu
    • 1
    • 2
  • Guan-Feng Liu
    • 1
    • 2
  • Kai Zheng
    • 1
  • Xiaofang Zhou
    • 1
  1. 1.School of Computer Science and TechnologySoochow UniversitySuzhouChina
  2. 2.Collaborative Innovation Center of Novel Software Technology and IndustrializationNanjingChina

Personalised recommendations