<?phpxml version="1.0" encoding="ISO-8859-1"?>
 <collection>
  

 
<record>
 <datafield tag="088" ind1="" ind2="">
  <subfield code="a">Pougala_STRC2023</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>
</datafield>
 <datafield tag="700" ind1="" ind2="">
  <subfield code="a">Pougala, Janody</subfield> 
  </datafield>
 <datafield tag="700" ind1="" ind2="">
  <subfield code="a">Hillel, Tim</subfield> 
  </datafield>
 <datafield tag="700" ind1="" ind2="">
  <subfield code="a">Bierlaire, Michel</subfield> 
  </datafield>
<datafield tag="245" ind1="" ind2="">
<subfield code="a">
From one-day to multiday activity scheduling: extending the OASIS framework</subfield>
</datafield>
<datafield tag="260" ind1="" ind2="">
<subfield code="c">2023</subfield>
</datafield>
<datafield tag="711" ind1="2" ind2="">
<subfield code="a">
STRC 2023</subfield>
<subfield code="c">
Monte Veritá, Ascona, Switzerland</subfield>
<subfield code="d">May 10, 2023</subfield>
</datafield>
<datafield tag="520" ind1="" ind2="">
<subfield code="a">
Applications of activity-based models for the estimation of transport demand
have demonstrated to achieve greater behavioural realism than traditional
trip-based models. However, state-of-the art models focus on single-day
schedules as focal points to their estimations, thus ignoring fundamental dynamics that explain individual behaviour over longer periods of time. Several
have highlighted the importance of multiday analyses in activity-travel contexts, which are still lacking in many state-of-the-art framework. In this paper,
we present an extension of the OASIS framework (Pougala et al., 2022),
an integrated model for the simulation of single day schedules, to include intrapersonal interactions influencing longer term decisions. We formulate the
multiday problem as a multiobjective optimisation problem where each day
d is associated with a utility Ud. We consider an activity-based set up where
individuals maximise the total utility of their schedules over multiple days
(e.g. week). We discuss implications and requirements of this formulation,
and illustrate the methodology with an application on the MOBIS dataset
(Molloy et al., 2021), an activity and travel survey capturing several weeks
of behaviour for each respondent.</subfield>
</datafield>
  </record>



  </collection>
