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dc.contributor.authorAuld, Joshua
dc.contributor.authorMohammadian, Abolfazl
dc.date.accessioned2011-05-25T22:24:46Z
dc.date.available2011-05-25T22:24:46Z
dc.date.issued2010
dc.identifier.bibliographicCitationAuld, J. & Mohammadian, A. 2010. Efficient Methodology for Generating Synthetic Populations with Multiple Control Levels. Transportation Research Record,(2175): 138-147. DOI: 10.3141/2175-16en
dc.identifier.issn0361-1981
dc.identifier.otherDOI: 10.3141/2175-16
dc.identifier.urihttp://hdl.handle.net/10027/7666
dc.descriptionPost print version of article may differ from published version. The definitive version is available through National Academy of Sciences at DOI: 10.3141/2175-16. © National Academy of Sciences. All Rights Reserved.en
dc.description.abstractThis paper details a new methodology for controlling attributes on multiple analysis levels within a population synthesis program. The methodology determines how both household- and personlevel characteristics can jointly be used as controls when synthesizing populations, as well as how other multiple level synthetic populations, such as firm/employee, household/vehicle, etc. can be estimated. The use of multi-level controls is implemented through a new technique involving the estimation of household selection probabilities based on the probability of observing each household given the required person-level characteristics in each analysis zone. The new procedure is a quick and efficient method for generating synthetic populations which can accurately replicate desired person-level characteristicsen
dc.description.sponsorshipThe authors would like to acknowledge the financial support provided to this project from the Chicago Metropolitan Agency for Planning (CMAP) and the National Science Foundation (NSF) Integrative Graduate Education and Research Traineeship (IGERT) program in Computational Transportation Science at UIC.en
dc.language.isoen_USen
dc.publisherNational Academy of Scienceen
dc.subjectpopulation synthesis programen
dc.subjectmultiple level synthetic populationsen
dc.titleAn efficient methodology for generating synthetic populations with multiple control levelsen
dc.title.alternativeEfficient methodology for generating synthetic populations with multiple control levelsen
dc.typeArticleen


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