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Marta C. González. Gupta, Siddharth, S.M. Massachusetts Institute of Technology Massachusetts Institute of Technology. Department of Civil and Environmental Engineering. Massachusetts Institute of Technology. Department of Civil and Environmental Engineering 2017-09-15T15:34:09Z 2017-09-15T15:34:09Z 2017 2017 http://hdl.handle.net/1721.1/111437 1003292704 Thesis: S.M. in Transportation, Massachusetts Institute of Technology, Department of Civil and Environmental Engineering, 2017. Cataloged from PDF version of thesis. Includes bibliographical references (pages 92-97). As geographic data about individual movement become increasingly available, they open LIP the possibility of understanding and modeling urban mobility patterns. While no all-encompassing dataset regarding mobility is available, this study explores how Call Detail Records (CDRs), a highly ubiquitous dataset, can be leveraged to create models that can reproduce mobility patterns observed from time consuming, capital-intensive and infrequent travel surveys. While mechanisms have been proposed for reproducing particular characteristics of individual mobility, this is the first attempt to generate all mobility patterns at fine spatial and temporal scales at the level of individual buildings. Two shortcomings of any dataset include spatial uncertainty at very high resolution and the presence of high-fidelity traces for only a fraction of the population. While the proposed model addressed the former to some extent by providing high accuracy counts at the level of census tracts, a separate method has been explored to address this along with the latter phenomenon. To achieve this, the study leverages hyper-local datasets such as building footprints and places of interest. In the absence of primary datasets, the study is able to provide a model to estimate of the presence of people at the level of individual buildings. Hence, this study provides a pipeline to proceed from high fidelity location traces from a fraction of the population to building level occupancy profiles using fairly ubiquitous data sources. by Siddharth Gupta. S.M. in Transportation 97 pages eng Massachusetts Institute of Technology MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission. http://dspace.mit.edu/handle/1721.1/7582 Civil and Environmental Engineering. Estimating the presence of people in buildings using Call Detail Records Thesis application/pdf Publication unknown unknown <Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="fa422bc1-2ae5-4a9c-a7b3-75adf03135cb"> <Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type> <Language>eng</Language> <Title>Estimating the presence of people in buildings using Call Detail Records</Title> <PublishedIn> <Publication> </Publication> </PublishedIn> <PublicationDate>2017</PublicationDate> <Authors> <Author> <DisplayName>Gupta, Siddharth, S.M. Massachusetts Institute of Technology</DisplayName> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> </Authors> <Editors> </Editors> <Publishers> <Publisher> <DisplayName>Massachusetts Institute of Technology</DisplayName> <OrgUnit /> </Publisher> </Publishers> <License>http://dspace.mit.edu/handle/1721.1/7582</License> <Keyword>Civil and Environmental Engineering.</Keyword> <Abstract>As geographic data about individual movement become increasingly available, they open LIP the possibility of understanding and modeling urban mobility patterns. While no all-encompassing dataset regarding mobility is available, this study explores how Call Detail Records (CDRs), a highly ubiquitous dataset, can be leveraged to create models that can reproduce mobility patterns observed from time consuming, capital-intensive and infrequent travel surveys. While mechanisms have been proposed for reproducing particular characteristics of individual mobility, this is the first attempt to generate all mobility patterns at fine spatial and temporal scales at the level of individual buildings. Two shortcomings of any dataset include spatial uncertainty at very high resolution and the presence of high-fidelity traces for only a fraction of the population. While the proposed model addressed the former to some extent by providing high accuracy counts at the level of census tracts, a separate method has been explored to address this along with the latter phenomenon. To achieve this, the study leverages hyper-local datasets such as building footprints and places of interest. In the absence of primary datasets, the study is able to provide a model to estimate of the presence of people at the level of individual buildings. Hence, this study provides a pipeline to proceed from high fidelity location traces from a fraction of the population to building level occupancy profiles using fairly ubiquitous data sources.</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication>