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dc.contributor.advisorCirillo, Cinziaen_US
dc.contributor.authorManess, Michaelen_US
dc.date.accessioned2015-07-17T05:30:57Z
dc.date.available2015-07-17T05:30:57Z
dc.date.issued2015en_US
dc.identifierhttps://doi.org/10.13016/M28H09
dc.identifier.urihttp://hdl.handle.net/1903/16775
dc.description.abstractUnderstanding the determinants of activities and travel is critical for transportation policymakers, planners, and engineers to design and manage transportation systems. These systems, and their externalities, are interwoven with social systems in communities, cities, regions, and societies. But discrete choice models - the predominant modeling tool for researching travel behavior and planning transportation systems - are grounded in theories of individual decision-making. This dissertation expands knowledge about the incorporation of social interactions into activity-travel choice models in the areas of social capital and social network indicators; social influence motivations and informational conformity; and misspecification errors from social network data collection. Incorporating social capital into activity choice models involves using social capital indicators from surveys. Using a position generator question type, the role of social network occupational diversity in activity participation was explored and the performance of models using name generator and position generator data was compared. Access to the resources embedded in diverse networks (extensity) was found to positively correlate with leisure activity participation. Compared to core network indicators from name generators, position generator indicators were typically better at predicting activity participation in a cross-validation study. Current models of social influence in travel do not account for varying motivations for social influence such as for accuracy, affiliation, and self-concept. To test for an accuracy motivation, a latent class discrete choice model was formulated that places individuals into classes based on information exposure. Contrasting with existing work, this model showed that "more informed" households are more likely to own bicycles due to preference changes causing less sensitivity to smaller home footprints and limited incomes. A Bayesian prediction procedure was used to derive distributions of local-level equilibria and social influence elasticity. The effect of errors in social network data collection using name and position generators is not fully understood for choice models. In a case study, the social network occupational diversity measure was robust to varying position generator lengths. Simulation experiments tested the implications of social network structure, misspecification, and small samples on social influence choice models where sample size, social influence strength, and degree of misspecification had the greatest impact.en_US
dc.language.isoenen_US
dc.titleChoice Modeling Perspectives on Social Networks, Social Influence, and Social Capital in Activity and Travel Behavioren_US
dc.typeDissertationen_US
dc.contributor.publisherDigital Repository at the University of Marylanden_US
dc.contributor.publisherUniversity of Maryland (College Park, Md.)en_US
dc.contributor.departmentCivil Engineeringen_US
dc.subject.pqcontrolledCivil engineeringen_US
dc.subject.pqcontrolledTransportation planningen_US
dc.subject.pqcontrolledEconomicsen_US
dc.subject.pquncontrolledBayesian Inferenceen_US
dc.subject.pquncontrolledBicycle Ownershipen_US
dc.subject.pquncontrolledDiscrete choiceen_US
dc.subject.pquncontrolledInformational Conformityen_US
dc.subject.pquncontrolledMisspecification Erroren_US
dc.subject.pquncontrolledPosition Generatoren_US


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