Journal article Open Access

Clustering Trajectories by Relevant Parts for Air Traffic Analysis

Andrienko, Gennady; Andrienko, Natalia; Fuchs, Georg; Garcia, Jose Manuel Cordero


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  <identifier identifierType="URL">https://zenodo.org/record/889198</identifier>
  <creators>
    <creator>
      <creatorName>Andrienko, Gennady</creatorName>
      <givenName>Gennady</givenName>
      <familyName>Andrienko</familyName>
      <affiliation>Fraunhofer IAIS, City University London</affiliation>
    </creator>
    <creator>
      <creatorName>Andrienko, Natalia</creatorName>
      <givenName>Natalia</givenName>
      <familyName>Andrienko</familyName>
      <affiliation>Fraunhofer IAIS, City University London</affiliation>
    </creator>
    <creator>
      <creatorName>Fuchs, Georg</creatorName>
      <givenName>Georg</givenName>
      <familyName>Fuchs</familyName>
      <affiliation>Fraunhofer IAIS</affiliation>
    </creator>
    <creator>
      <creatorName>Garcia, Jose Manuel Cordero</creatorName>
      <givenName>Jose Manuel Cordero</givenName>
      <familyName>Garcia</familyName>
      <affiliation>CRIDA</affiliation>
    </creator>
  </creators>
  <titles>
    <title>Clustering Trajectories by Relevant Parts for Air Traffic Analysis</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2017</publicationYear>
  <subjects>
    <subject>Trajectory</subject>
    <subject>Data visualization</subject>
    <subject>Three-dimensional displays</subject>
    <subject>Guidelines</subject>
    <subject>Visualization</subject>
    <subject>Clustering algorithms</subject>
    <subject>Algorithm design and analysis</subject>
  </subjects>
  <dates>
    <date dateType="Issued">2017-08-29</date>
  </dates>
  <resourceType resourceTypeGeneral="Text">Journal article</resourceType>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/889198</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="URL" relationType="IsIdenticalTo">http://geoanalytics.net/and/papers/vast17.pdf</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsIdenticalTo">10.1109/TVCG.2017.2744322</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="URL" relationType="IsPartOf">https://zenodo.org/communities/h2020_datacron</relatedIdentifier>
  </relatedIdentifiers>
  <rightsList>
    <rights rightsURI="http://creativecommons.org/licenses/by-nc/4.0/legalcode">Creative Commons Attribution Non Commercial 4.0 International</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
  </rightsList>
  <descriptions>
    <description descriptionType="Abstract">&lt;p&gt;Clustering of trajectories of moving objects by similarity is an important technique in movement analysis. Existing distance functions assess the similarity between trajectories based on properties of the trajectory points or segments. The properties may include the spatial positions, times, and thematic attributes. There may be a need to focus the analysis on certain parts of trajectories, i.e., points and segments that have particular properties. According to the analysis focus, the analyst may need to cluster trajectories by similarity of their relevant parts only. Throughout the analysis process, the focus may change, and different parts of trajectories may become relevant. We propose an analytical workflow in which interactive filtering tools are used to attach relevance flags to elements of trajectories, clustering is done using a distance function that ignores irrelevant elements, and the resulting clusters are summarized for further analysis. We demonstrate how this workflow can be useful for different analysis tasks in three case studies with real data from the domain of air traffic. We propose a suite of generic techniques and visualization guidelines to support movement data analysis by means of relevance-aware trajectory clustering.&lt;/p&gt;</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>
    <fundingReference>
      <funderName>European Commission</funderName>
      <funderIdentifier funderIdentifierType="Crossref Funder ID">10.13039/501100000780</funderIdentifier>
      <awardNumber awardURI="info:eu-repo/grantAgreement/EC/H2020/699303/">699303</awardNumber>
      <awardTitle>Interactive Toolset for Understanding Trade-offs in ATM Performance</awardTitle>
    </fundingReference>
  </fundingReferences>
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