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Unveiling Movement Uncertainty for Robust Trajectory Similarity Analysis

Andre Salvaro Furtado; Luis Otavio Campos Alvares; Nikos Pelekis; Yannis Theodoridis; Vania Bogorny


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  <identifier identifierType="URL">https://zenodo.org/record/2532989</identifier>
  <creators>
    <creator>
      <creatorName>Andre Salvaro Furtado</creatorName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0002-3714-7167</nameIdentifier>
      <affiliation>Instituto Federal de Santa Catarina, Xanxere, Brazil</affiliation>
    </creator>
    <creator>
      <creatorName>Luis Otavio Campos Alvares</creatorName>
      <affiliation>PPGCC, INE, Universidade Federal de Santa Catarina, Florianopolis, Brazil</affiliation>
    </creator>
    <creator>
      <creatorName>Nikos Pelekis</creatorName>
      <affiliation>University of Piraeus, Piraeus, Greece</affiliation>
    </creator>
    <creator>
      <creatorName>Yannis Theodoridis</creatorName>
      <affiliation>University of Piraeus, Piraeus, Greece</affiliation>
    </creator>
    <creator>
      <creatorName>Vania Bogorny</creatorName>
      <affiliation>PPGCC, INE, Universidade Federal de Santa Catarina, Florianopolis, Brazil</affiliation>
    </creator>
  </creators>
  <titles>
    <title>Unveiling Movement Uncertainty for Robust Trajectory Similarity Analysis</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2017</publicationYear>
  <subjects>
    <subject>Movement Similarity, Raw Trajectory Similarity, Elliptical Trajectory Representation, Dynamic Threshold Similarity, Parameter free Similarity Measure</subject>
  </subjects>
  <dates>
    <date dateType="Issued">2017-09-10</date>
  </dates>
  <language>en</language>
  <resourceType resourceTypeGeneral="Text">Preprint</resourceType>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/2532989</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsIdenticalTo">10.1080/13658816.2017.1372763</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="URL" relationType="IsPartOf">https://zenodo.org/communities/h2020_datacron</relatedIdentifier>
  </relatedIdentifiers>
  <rightsList>
    <rights rightsURI="http://creativecommons.org/licenses/by/4.0/legalcode">Creative Commons Attribution 4.0 International</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
  </rightsList>
  <descriptions>
    <description descriptionType="Abstract">&lt;p&gt;Trajectory data analysis and mining require distance and similarity measures, and the quality of their results is directly related to those measures. Several similarity measures originally proposed for time-series were adapted to work with trajectory data, but these approaches were developed for well-behaved data, that usually do not have the uncertainty and heterogeneity introduced by the sampling process to obtain trajectories. More recently, similarity measures were proposed specifically for trajectory data, but they rely on simplistic movement uncertainty representations, such as linear interpolation. In this article we propose a new distance function, and a new similarity measure that uses an elliptical representation of trajectories, being more robust to the movement uncertainty caused by the sampling rate and the heterogeneity of this kind of data. Experiments using real data show that our proposal is more accurate and robust than related work.&lt;/p&gt;</description>
    <description descriptionType="Other">Andre Salvaro Furtado, Luis Otavio Campos Alvares, Nikos Pelekis, Yannis Theodoridis &amp; Vania Bogorny (2018) Unveiling movement uncertainty for robust trajectory similarity analysis, International Journal of Geographical Information Science, 32:1, 140-168, DOI: 10.1080/13658816.2017.1372763</description>
  </descriptions>
  <fundingReferences>
    <fundingReference>
      <funderName>European Commission</funderName>
      <funderIdentifier funderIdentifierType="Crossref Funder ID">10.13039/501100000780</funderIdentifier>
      <awardNumber awardURI="info:eu-repo/grantAgreement/EC/H2020/687591/">687591</awardNumber>
      <awardTitle>Big Data Analytics for Time Critical Mobility Forecasting</awardTitle>
    </fundingReference>
  </fundingReferences>
</resource>
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