Analyzing Trajectory Gaps for Possible Rendezvous: A Summary of Results

Authors Arun Sharma, Xun Tang, Jayant Gupta, Majid Farhadloo, Shashi Shekhar

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Author Details

Arun Sharma
  • Department of Computer Science & Engineering, University of Minnesota, Minneapolis, MN, USA
Xun Tang
  • Department of Computer Science & Engineering, University of Minnesota, Minneapolis, MN, USA
Jayant Gupta
  • Department of Computer Science & Engineering, University of Minnesota, Minneapolis, MN, USA
Majid Farhadloo
  • Department of Computer Science & Engineering, University of Minnesota, Minneapolis, MN, USA
Shashi Shekhar
  • Department of Computer Science & Engineering, University of Minnesota, Minneapolis, MN, USA


We would also like to thank Kim Koffolt and spatial computing research group for their helpful comments and refinements.

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Arun Sharma, Xun Tang, Jayant Gupta, Majid Farhadloo, and Shashi Shekhar. Analyzing Trajectory Gaps for Possible Rendezvous: A Summary of Results. In 11th International Conference on Geographic Information Science (GIScience 2021) - Part I. Leibniz International Proceedings in Informatics (LIPIcs), Volume 177, pp. 13:1-13:16, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2020)


Given trajectory data with gaps, we investigate methods to identify possible rendezvous regions. Societal applications include improving maritime safety and regulations. The challenges come from two aspects. If trajectory data are not available around the rendezvous then either linear or shortest-path interpolation may fail to detect the possible rendezvous. Furthermore, the problem is computationally expensive due to the large number of gaps and associated trajectories. In this paper, we first use the plane sweep algorithm as a baseline. Then we propose a new filtering framework using the concept of a space-time grid. Experimental results and case study on real-world maritime trajectory data show that the proposed approach substantially improves the Area Pruning Efficiency over the baseline technique.

Subject Classification

ACM Subject Classification
  • Information systems → Data mining
  • Computing methodologies → Spatial and physical reasoning
  • Spatial data mining
  • trajectory mining
  • time geography


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  1. Elena Camossi, Paola Villa, and Luca Mazzola. Semantic-based anomalous pattern discovery in moving object trajectories. arXiv preprint arXiv:1305.1946, 2013. Google Scholar
  2. Somayeh Dodge, Robert Weibel, and Anna-Katharina Lautenschütz. Towards a taxonomy of movement patterns. Information visualization, 7(3-4):240-252, 2008. Google Scholar
  3. Emre Eftelioglu, Xun Tang, and Shashi Shekhar. Avoidance region discovery: A summary of results. In Proceedings of the 2018 SIAM International Conference on Data Mining, pages 585-593. SIAM, 2018. Google Scholar
  4. Kathleen Hornsby and Max J Egenhofer. Modeling moving objects over multiple granularities. Annals of Mathematics and Artificial Intelligence, 36(1-2):177-194, 2002. Google Scholar
  5. Edwin H Jacox and Hanan Samet. Spatial join techniques. ACM Transactions on Database Systems (TODS), 32(1):7-es, 2007. Google Scholar
  6. Hyun-Mi Kim and Mei-Po Kwan. Space-time accessibility measures: A geocomputational algorithm with a focus on the feasible opportunity set and possible activity duration. Journal of geographical Systems, 5(1):71-91, 2003. Google Scholar
  7. Bart Kuijpers, Rafael Grimson, and Walied Othman. An analytic solution to the alibi query in the space-time prisms model for moving object data. International Journal of Geographical Information Science, 25(2):293-322, 2011. Google Scholar
  8. Bart Kuijpers, Harvey J Miller, Tijs Neutens, and Walied Othman. Anchor uncertainty and space-time prisms on road networks. International Journal of Geographical Information Science, 24(8):1223-1248, 2010. Google Scholar
  9. Bart Kuijpers, Harvey J Miller, and Walied Othman. Kinetic prisms: incorporating acceleration limits into space-time prisms. International Journal of Geographical Information Science, 31(11):2164-2194, 2017. Google Scholar
  10. Bart Kuijpers and Walied Othman. Modeling uncertainty of moving objects on road networks via space-time prisms. International Journal of Geographical Information Science, 23(9):1095-1117, 2009. Google Scholar
  11. Mei-Po Kwan. Gis methods in time-geographic research: Geocomputation and geovisualization of human activity patterns. Geografiska Annaler: Series B, Human Geography, 86(4):267-280, 2004. Google Scholar
  12. Po-Ruey Lei. A framework for anomaly detection in maritime trajectory behavior. Knowledge and Information Systems, 47(1):189-214, 2016. Google Scholar
  13. Bo Liu, Erico N de Souza, Cassey Hilliard, and Stan Matwin. Ship movement anomaly detection using specialized distance measures. In 2015 18th International Conference on Information Fusion (Fusion), pages 1113-1120. IEEE, 2015. Google Scholar
  14. Ahmed R Mahmood, Walid G Aref, Ahmed M Aly, and Saleh Basalamah. Indexing recent trajectories of moving objects. In Proceedings of the 22nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, pages 393-396, 2014. Google Scholar
  15. URL:
  16. Harvey J Miller. Modelling accessibility using space-time prism concepts within geographical information systems. International Journal of Geographical Information System, 5(3):287-301, 1991. Google Scholar
  17. Tijs Neutens, Tim Schwanen, and Frank Witlox. The prism of everyday life: Towards a new research agenda for time geography. Transport reviews, 31(1):25-47, 2011. Google Scholar
  18. Jürg Nievergelt and Franco P. Preparata. Plane-sweep algorithms for intersecting geometric figures. Communications of the ACM, 25(10):739-747, 1982. Google Scholar
  19. Andrey Tietbohl Palma, Vania Bogorny, Bart Kuijpers, and Luis Otavio Alvares. A clustering-based approach for discovering interesting places in trajectories. In Proceedings of the 2008 ACM symposium on Applied computing, pages 863-868, 2008. Google Scholar
  20. Dieter Pfoser, Christian S Jensen, Yannis Theodoridis, et al. Novel approaches to the indexing of moving object trajectories. In VLDB, pages 395-406, 2000. Google Scholar
  21. Maria Riveiro, Giuliana Pallotta, and Michele Vespe. Maritime anomaly detection: A review. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 8(5):e1266, 2018. Google Scholar
  22. Jose Antonio MR Rocha, Valéria C Times, Gabriel Oliveira, Luis O Alvares, and Vania Bogorny. Db-smot: A direction-based spatio-temporal clustering method. In 2010 5th IEEE international conference intelligent systems, pages 114-119. IEEE, 2010. Google Scholar
  23. Hamed Yaghoubi Shahir, Uwe Glässer, Narek Nalbandyan, and Hans Wehn. Maritime situation analysis: A multi-vessel interaction and anomaly detection framework. In 2014 IEEE Joint Intelligence and Security Informatics Conference, pages 192-199. IEEE, 2014. Google Scholar
  24. Katerina Sofrona. Why cannot i see a vessel on the live map?, October 2017. URL:
  25. Stefano Spaccapietra, Christine Parent, Maria Luisa Damiani, Jose Antonio de Macedo, Fabio Porto, and Christelle Vangenot. A conceptual view on trajectories. Data & knowledge engineering, 65(1):126-146, 2008. Google Scholar
  26. Goce Trajcevski, Alok Choudhary, Ouri Wolfson, Li Ye, and Gang Li. Uncertain range queries for necklaces. In 2010 Eleventh International Conference on Mobile Data Management, pages 199-208. IEEE, 2010. Google Scholar
  27. Yu Zheng. Trajectory data mining: an overview. ACM Transactions on Intelligent Systems and Technology (TIST), 6(3):1-41, 2015. Google Scholar
  28. Qing Zhu, Jun Gong, and Yeting Zhang. An efficient 3d r-tree spatial index method for virtual geographic environments. ISPRS Journal of Photogrammetry and Remote Sensing, 62(3):217-224, 2007. Google Scholar