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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

Funding

This material is based upon work supported by the US Department Of Defense Grant No. HM04762010009 and National Science Foundation under Grants No. 1737633.

Acknowledgements

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

Cite As Get BibTex

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) https://doi.org/10.4230/LIPIcs.GIScience.2021.I.13

Abstract

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
Keywords
  • Spatial data mining
  • trajectory mining
  • time geography

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