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 <datafield tag="088" ind1="" ind2="">
  <subfield code="a">Glerum_WDCA_13</subfield> 
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<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">Glerum, Aurélie</subfield> 
  </datafield>
 <datafield tag="700" ind1="" ind2="">
  <subfield code="a">Hurtubia, Ricardo</subfield> 
  </datafield>
 <datafield tag="700" ind1="" ind2="">
  <subfield code="a">Nguyen, My Hang</subfield> 
  </datafield>
 <datafield tag="700" ind1="" ind2="">
  <subfield code="a">Bierlaire, Michel</subfield> 
  </datafield>
<datafield tag="245" ind1="" ind2="">
<subfield code="a">
Enhanced measurement equations for latent class choice models</subfield>
</datafield>
<datafield tag="260" ind1="" ind2="">
<subfield code="c">2013</subfield>
</datafield>
<datafield tag="711" ind1="2" ind2="">
<subfield code="a">
Eighth workshop on discrete choice models</subfield>
<subfield code="c">
EPFL, Lausanne, Switzerland</subfield>
<subfield code="d">May 31, 2013</subfield>
</datafield>
<datafield tag="520" ind1="" ind2="">
<subfield code="a">
Integrating psychometric indicators in latent class choice models allows to enhance the characterization of the latent classes. In this research, we consider measurement equations that include socio-economic indicators of the decision-makers. We show that such a specification increases the significance of the parameters relative to the class-membership relation and leads to a better interpretability of the behavior of individuals in the latent classes. The method is applied to a transportation mode choice case study in Switzerland.</subfield>
</datafield>
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