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  <subfield code="a">hEART_danalet_2013</subfield> 
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<subfield code="p">TRANSP-OR</subfield>
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<subfield code="a">TALK</subfield>
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  <subfield code="a">Danalet, Antonin</subfield> 
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  <subfield code="a">Bierlaire, Michel</subfield> 
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  <subfield code="a">Farooq, Bilal</subfield> 
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<datafield tag="245" ind1="" ind2="">
<subfield code="a">
A Pedestrian Destination-Chain Choice Model from Bayesian Estimation of Pedestrian Activities using Sensors Data</subfield>
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<subfield code="c">2013</subfield>
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<subfield code="a">
2nd Symposium of the European Association for Research in Transportation (hEART 2013)</subfield>
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Stockholm, Sweden</subfield>
<subfield code="d">September 05, 2013</subfield>
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Pedestrian modeling is emerging as a tool for designing new infrastructures and optimizing the use of current ones. Given sensor traces, we are interested in developing a dynamic model that can predict the destination chain of an individual in pedestrian facilities. As a first step, we developed a methodology to collect activity-episodes sequences from scarce data, directly modeling the imprecision in the measure. It generates several candidate lists of activity-episodes sequences associated with a corresponding likelihood.</subfield>
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<subfield code="a">EPFL-TALK-188384</subfield>
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