Abstract
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.
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References
Andersson M, Gudmundsson J, Laube P, Wolle T (2008) Reporting leaders and followers among trajectories of moving point objects. GeoInformatica 12:497–528
Andrienko N, Andrienko G (2007) Designing visual analytics methods for massive collections of movement data. Cartographica 42:117–138
Aung HH, Tan KL 2010 Discovery of evolving convoys. In: Proceedings of the 22nd international conference on scientific and statistical database management, pp 196–213
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)
Dodge S, Weibel R, Lautenschütz A-K (2008) Towards a taxonomy of movement patterns. Inf Visual 7(3–4):240–252
Gudmundsson J, Van Kreveld M, Speckmann B (2004) Efficient detection of motion patterns in spatio-temporal data sets. In: Proceedings of the ACM GIS
Gudmundsson J, Van Kreveld (2006) Computing longest duration flocks in trajectory data. In: Proceedings of the ACM GIS
Helbing D, Molnar P (1995) Social force model for pedestrian dynamics. Phys Rev 51(5):4282–4286
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 90–105
Jeung H, Yiu ML, Zhou X, Jensen CS, Shen HT (2008) Discovery of convoys in trajectory databases. In: Proceedings of the VLDB
Kalnis P, Mamoulis N, Bakiras S (2005) On discovering moving clusters in spatial-temporal data. In: Proceedings of the SSTD
Laube P, Imfeld S (2002) Analyzing relative motion within groups of trackable moving point objects. In: Proceedings of the Geographic information science, pp 132–144
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 201–214
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 VLDB
Romero AOC (2011) Mining moving flock patterns in large spatio-temporal datasets using a frequent pattern mining approach. Master thesis, University of Twente, faculty ITC
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 ICDE
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 286–295
Yoon H, Shahabi C (2009) Accurate discovery of valid convoys from moving object trajectories. In: IEEE international conference on data mining workshops, pp: 636–643
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 99–108
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, ACM
Zheng Y (2011) T-Drive trajectory data sample, viewed 30 June 2012, http://research.microsoft.com/apps/pubs/?id=152883
Acknowledgments
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.
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Kuntzsch, C., Bohn, A. (2013). A Framework for On-line Detection of Custom Group Movement Patterns. In: Krisp, J. (eds) Progress in Location-Based Services. Lecture Notes in Geoinformation and Cartography. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34203-5_6
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DOI: https://doi.org/10.1007/978-3-642-34203-5_6
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