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 <datafield tag="088" ind1="" ind2="">
  <subfield code="a">2024Delft_ABM</subfield> 
  </datafield>
<datafield tag="909" ind1="C" ind2="0">
<subfield code="p">TRANSP-OR</subfield>
</datafield>
<datafield tag="980" ind1="" ind2="">
<subfield code="a">TALK</subfield>
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 <datafield tag="700" ind1="" ind2="">
  <subfield code="a">Rezvany, Negar</subfield> 
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 <datafield tag="700" ind1="" ind2="">
  <subfield code="a">Bierlaire, Michel</subfield> 
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<datafield tag="245" ind1="" ind2="">
<subfield code="a">
Activity-based models: an optimization approach</subfield>
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<datafield tag="260" ind1="" ind2="">
<subfield code="c">2024</subfield>
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<datafield tag="711" ind1="2" ind2="">
<subfield code="a">
Seminar of the Department of Mechanical Engineering</subfield>
<subfield code="c">
TU Delft, The Netherlands</subfield>
<subfield code="d">November 13, 2024</subfield>
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<datafield tag="520" ind1="" ind2="">
<subfield code="a">
Modern transportation systems require advanced travel demand models to accurately capture and predict travel behavior. Activity-based models offer a comprehensive approach by forecasting all daily activities and the resulting travel patterns. However, traditional choice models struggle to handle the large number of alternatives involved in such complex scenarios. To address this issue, we propose a novel modeling approach that leverages combinatorial optimization techniques, effectively managing the complexity and improving predictive performance.
</subfield>
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