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        <identifier>oai:ipsj.ixsq.nii.ac.jp:00224216</identifier>
        <datestamp>2025-01-19T13:13:15Z</datestamp>
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          <dc:title>Generic Trip Purpose Inference Modelling on Trip Chain</dc:title>
          <dc:title>Generic Trip Purpose Inference Modelling on Trip Chain</dc:title>
          <dc:creator>Suxing, Lyu</dc:creator>
          <dc:creator>Yuuki, Nishiyama</dc:creator>
          <dc:creator>Kaoru, Sezaki</dc:creator>
          <dc:creator>Takahiko, Kusakabe</dc:creator>
          <dc:creator>Suxing, Lyu</dc:creator>
          <dc:creator>Yuuki, Nishiyama</dc:creator>
          <dc:creator>Kaoru, Sezaki</dc:creator>
          <dc:creator>Takahiko, Kusakabe</dc:creator>
          <dc:subject>博士論文</dc:subject>
          <dc:description>Urban transportation systems are under increasing pressure from rapid population growth. At the same time, urban functions are becoming complex and diversified. In this context, the rapid transformation of cities has long promoted the demand for human mobility (travel behavior) analysis. Trip purpose, one of the behavioral factors, is crucial to understanding human mobility generation. But keeping track of trip purpose is not easy. Nowadays, there is a huge volume of human mobility data passively collected by mobile devices. Trip purpose is right the missing item form the data. The more reliable we are in the inference of the missing items, the more beneficial we can get from the data. A generic trip purpose inference can bring semantic information to human mobility. Consequently, the decision of urban transportation and urban infrastructure development will be improved and supported based on the understanding of human mobility generation. The doctoral study reports the findings on developing the generic privacy-insensitive trip purpose inference for one-day travel (trip chain).</dc:description>
          <dc:description>Urban transportation systems are under increasing pressure from rapid population growth. At the same time, urban functions are becoming complex and diversified. In this context, the rapid transformation of cities has long promoted the demand for human mobility (travel behavior) analysis. Trip purpose, one of the behavioral factors, is crucial to understanding human mobility generation. But keeping track of trip purpose is not easy. Nowadays, there is a huge volume of human mobility data passively collected by mobile devices. Trip purpose is right the missing item form the data. The more reliable we are in the inference of the missing items, the more beneficial we can get from the data. A generic trip purpose inference can bring semantic information to human mobility. Consequently, the decision of urban transportation and urban infrastructure development will be improved and supported based on the understanding of human mobility generation. The doctoral study reports the findings on developing the generic privacy-insensitive trip purpose inference for one-day travel (trip chain).</dc:description>
          <dc:description>technical report</dc:description>
          <dc:publisher>情報処理学会</dc:publisher>
          <dc:date>2023-02-21</dc:date>
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          <dc:identifier>研究報告モバイルコンピューティングと新社会システム（MBL）</dc:identifier>
          <dc:identifier>24</dc:identifier>
          <dc:identifier>2023-MBL-106</dc:identifier>
          <dc:identifier>1</dc:identifier>
          <dc:identifier>2</dc:identifier>
          <dc:identifier>2188-8817</dc:identifier>
          <dc:identifier>AA11851388</dc:identifier>
          <dc:identifier>https://ipsj.ixsq.nii.ac.jp/record/224216/files/IPSJ-MBL23106024.pdf</dc:identifier>
          <dc:language>eng</dc:language>
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