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  <subfield code="a">FloeTUB2011</subfield> 
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<datafield tag="909" ind1="C" ind2="0">
<subfield code="p">TRANSP-OR</subfield>
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<datafield tag="980" ind1="" ind2="">
<subfield code="a">TALK</subfield>
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 <datafield tag="700" ind1="" ind2="">
  <subfield code="a">Flötteröd, Gunnar</subfield> 
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  <subfield code="a">Bierlaire, Michel</subfield> 
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<datafield tag="245" ind1="" ind2="">
<subfield code="a">
Choice set generation for iterated DTA simulations</subfield>
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<datafield tag="260" ind1="" ind2="">
<subfield code="c">2011</subfield>
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<datafield tag="711" ind1="2" ind2="">
<subfield code="a">
internal seminar</subfield>
<subfield code="c">
TU Berlin, Berlin, Germany</subfield>
<subfield code="d">May 04, 2011</subfield>
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<datafield tag="520" ind1="" ind2="">
<subfield code="a">
We apply the Metropolis-Hastings algorithm to efficiently sample from arbitrary paths distributions in a general network. Paths can be generalized into all-day travel plans through, e.g., an appropriate network expansion. The Metropolis-Hastings algorithm creates a Markov chain of paths, which resembles DTA simulations that can also be phrased as Markov chains. A combination of both chains could lead to better understood DTA simulations that avoid the arbitrariness of current choice set generation procedures. </subfield>
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