Analyzing Trajectory Gaps for Possible Rendezvous: A Summary of Results

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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 round 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.

Original languageEnglish (US)
Title of host publication11th International Conference on Geographic Information Science, GIScience 2021
EditorsKrzysztof Janowicz, Judith A. Verstegen
PublisherSchloss Dagstuhl- Leibniz-Zentrum fur Informatik GmbH, Dagstuhl Publishing
ISBN (Electronic)9783959771665
DOIs
StatePublished - Sep 1 2020
Event11th International Conference on Geographic Information Science, GIScience 2021 - Poznan, Poland
Duration: Sep 27 2021Sep 30 2021

Publication series

NameLeibniz International Proceedings in Informatics, LIPIcs
Volume177
ISSN (Print)1868-8969

Conference

Conference11th International Conference on Geographic Information Science, GIScience 2021
CountryPoland
CityPoznan
Period9/27/219/30/21

Bibliographical note

Funding Information:
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.

Publisher Copyright:
© LIPIcs 2020.

Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.

Keywords

  • Spatial data mining
  • time geography
  • trajectory mining

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