License: Creative Commons Attribution 3.0 Unported license (CC BY 3.0)
When quoting this document, please refer to the following
DOI: 10.4230/LIPIcs.GIScience.2021.I.13
URN: urn:nbn:de:0030-drops-130482
URL: http://dagstuhl.sunsite.rwth-aachen.de/volltexte/2020/13048/
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Sharma, Arun ; Tang, Xun ; Gupta, Jayant ; Farhadloo, Majid ; Shekhar, Shashi

Analyzing Trajectory Gaps for Possible Rendezvous: A Summary of Results

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LIPIcs-GIScience-2021-I-13.pdf (2 MB)

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.

BibTeX - Entry

@InProceedings{sharma_et_al:LIPIcs:2020:13048,
  author =	{Arun Sharma and Xun Tang and Jayant Gupta and Majid Farhadloo and Shashi Shekhar},
  title =	{{Analyzing Trajectory Gaps for Possible Rendezvous: A Summary of Results}},
  booktitle =	{11th International Conference on Geographic Information Science (GIScience 2021) - Part I},
  pages =	{13:1--13:16},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-166-5},
  ISSN =	{1868-8969},
  year =	{2020},
  volume =	{177},
  editor =	{Krzysztof Janowicz and Judith A. Verstegen},
  publisher =	{Schloss Dagstuhl--Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/opus/volltexte/2020/13048},
  URN =		{urn:nbn:de:0030-drops-130482},
  doi =		{10.4230/LIPIcs.GIScience.2021.I.13},
  annote =	{Keywords: Spatial data mining, trajectory mining, time geography}
}

Keywords: Spatial data mining, trajectory mining, time geography
Collection: 11th International Conference on Geographic Information Science (GIScience 2021) - Part I
Issue Date: 2020
Date of publication: 25.09.2020

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