A Framework for On-line Detection of Custom Group Movement Patterns

  • Colin Kuntzsch
  • Alexander Bohn
Part of the Lecture Notes in Geoinformation and Cartography book series (LNGC)


This chapter describes a lightweight approach for custom definition and detection of group patterns in a real-time analysis scenario, using a simple, yet flexible notion for groups of moving point objects (MPOs) travelling together. Groups are defined as sets of MPOs that are directly or transitively related to each other via freely definable binary relations. Group candidates are identified within a snapshot view of the MPOs at discrete time instances. By backtracking over previous snapshots, stable group compositions over previous time instances are identified and reported. We give insight about the used data structures and algorithms for the group candidate calculation and backtracking steps and illustrate the approach’s functionality with examples from a real data set.


Pattern recognition Group motion Real-time movement analysis 



The funding of this research by the BMBF is gratefully acknowledged. We also want to thank the researchers of the T-Drive project for making their taxi trajectory data set publicly available. Finally, the authors would like to acknowledge an anonymous reviewer for his/her helpful comments on an earlier draft of this manuscript.


  1. Andersson M, Gudmundsson J, Laube P, Wolle T (2008) Reporting leaders and followers among trajectories of moving point objects. GeoInformatica 12:497–528CrossRefGoogle Scholar
  2. Andrienko N, Andrienko G (2007) Designing visual analytics methods for massive collections of movement data. Cartographica 42:117–138CrossRefGoogle Scholar
  3. Aung HH, Tan KL 2010 Discovery of evolving convoys. In: Proceedings of the 22nd international conference on scientific and statistical database management, pp 196–213Google Scholar
  4. Buchin K, Buchin M, Gudmundsson J (2008) Detecting single file movement. In: Proceedings of 16th ACM SIGSPATIAL international conference on advances in geographic information systems (ACM GIS)Google Scholar
  5. Dodge S, Weibel R, Lautenschütz A-K (2008) Towards a taxonomy of movement patterns. Inf Visual 7(3–4):240–252CrossRefGoogle Scholar
  6. Gudmundsson J, Van Kreveld M, Speckmann B (2004) Efficient detection of motion patterns in spatio-temporal data sets. In: Proceedings of the ACM GISGoogle Scholar
  7. Gudmundsson J, Van Kreveld (2006) Computing longest duration flocks in trajectory data. In: Proceedings of the ACM GISGoogle Scholar
  8. Helbing D, Molnar P (1995) Social force model for pedestrian dynamics. Phys Rev 51(5):4282–4286Google Scholar
  9. Huang Y, Chen C, Dong P (2008) Modeling herds and their evolvements from trajectory data. In: Cova TJ, Miller H, Beard K, Frank AU, Goodchild MF (eds) Geographic information science. Springer, Berlin, pp 90–105CrossRefGoogle Scholar
  10. Jeung H, Yiu ML, Zhou X, Jensen CS, Shen HT (2008) Discovery of convoys in trajectory databases. In: Proceedings of the VLDBGoogle Scholar
  11. Kalnis P, Mamoulis N, Bakiras S (2005) On discovering moving clusters in spatial-temporal data. In: Proceedings of the SSTDGoogle Scholar
  12. Laube P, Imfeld S (2002) Analyzing relative motion within groups of trackable moving point objects. In: Proceedings of the Geographic information science, pp 132–144Google Scholar
  13. Laube P, Van Kreveld M, Imfeld S (2004) Finding REMO—detecting relative motion patterns in geospatial lifelines. In: Fisher PF (ed) Developments in spatial data handling, Proceedings of the 11th international symposium on spatial data handling. Springer, Berlin, pp 201–214Google Scholar
  14. Li Z, Ding B, Han J, Kays R (2010) Swarm: mining relaxed temporal moving object clusters accurate discovery of valid convoys from moving object trajectories. In: Proceedings of the VLDBGoogle Scholar
  15. Romero AOC (2011) Mining moving flock patterns in large spatio-temporal datasets using a frequent pattern mining approach. Master thesis, University of Twente, faculty ITCGoogle Scholar
  16. Tang L-A, Zheng Y, Yuan J, Han J, Leung A, Hung C-C, Peng W-C (2012) On discovery of traveling companions from streaming trajectories. In: Proceedings of the ICDEGoogle Scholar
  17. Vieira MR, Bakalov P, Tsotras VJ (2009) On-line discovery of flock patterns in spatio-temporal data. In: Proceedings of the Geographic information science 2009, New York, pp 286–295Google Scholar
  18. Yoon H, Shahabi C (2009) Accurate discovery of valid convoys from moving object trajectories. In: IEEE international conference on data mining workshops, pp: 636–643Google Scholar
  19. Yuan J, Zheng Y, Zhang C, Xie W, Xie X, Sun G, Huang Y (2010) T-drive: driving directions based on taxi trajectories. In Proceedings of the 18th SIGSPATIAL international conference on advances in geographic information systems, GIS 2010, New York, ACM, pp 99–108Google Scholar
  20. Yuan J, Zheng Y, Xie X, Sun G (2011) Driving with knowledge from the physical world. In The 17th ACM SIGKDD international conference on knowledge discovery and data mining, KDD 2011, New York, ACMGoogle Scholar
  21. Zheng Y (2011) T-Drive trajectory data sample, viewed 30 June 2012,

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Institute of Cartography and GeoinformaticsLeibniz Universität HannoverHannoverGermany

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