Conference paper Open Access

Understanding of representative 24h travel activity sequences of Londoners

Chen, Yiqiao; Silva, Elisabete A.


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  <identifier identifierType="DOI">10.5281/zenodo.4665386</identifier>
  <creators>
    <creator>
      <creatorName>Chen, Yiqiao</creatorName>
      <givenName>Yiqiao</givenName>
      <familyName>Chen</familyName>
      <affiliation>Lab of Interdisciplinary Spatial Analysis, Department of Land Economy, University of Cambridge</affiliation>
    </creator>
    <creator>
      <creatorName>Silva, Elisabete A.</creatorName>
      <givenName>Elisabete A.</givenName>
      <familyName>Silva</familyName>
      <affiliation>Lab of Interdisciplinary Spatial Analysis, Department of Land Economy, University of Cambridge</affiliation>
    </creator>
  </creators>
  <titles>
    <title>Understanding of representative 24h travel activity sequences of Londoners</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2021</publicationYear>
  <dates>
    <date dateType="Issued">2021-04-06</date>
  </dates>
  <resourceType resourceTypeGeneral="ConferencePaper"/>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/4665386</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsVersionOf">10.5281/zenodo.4665385</relatedIdentifier>
  </relatedIdentifiers>
  <rightsList>
    <rights rightsURI="https://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;A 24-hour travel activity sequence is a traveller&amp;rsquo;s time-use activity in a day, containing location, function, purpose, and trip mode information in each time interval. Understanding the daily time-use profile of travellers can reveal the travel patterns in a city, uncover the travel behaviour of citizens, and increase the accuracy of activity-based travel forecasting models. The focus of this paper is to cluster the 24h travel activity sequences and learn the daily travel activity patterns of Londoners. We analyse the National Travel Survey data. A three-stage clustering algorithm is applied to group similar travel sequences and find representative travel patterns.&lt;/p&gt;</description>
  </descriptions>
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