Traffic Analysis on Cumulative Land Development and Transportation Related Policy Scenarios

dc.contributor.advisorZhang, Leien_US
dc.contributor.authorZhu, Zhengen_US
dc.contributor.departmentCivil Engineeringen_US
dc.contributor.publisherDigital Repository at the University of Marylanden_US
dc.contributor.publisherUniversity of Maryland (College Park, Md.)en_US
dc.date.accessioned2015-02-06T06:50:50Z
dc.date.available2015-02-06T06:50:50Z
dc.date.issued2014en_US
dc.description.abstractNumerous methods have been developed to evaluate the impact of land developments and transportation policies on transportation infrastructures. But traditional approaches are either limited to static performance or a lack of behavior foundation. With only a few activity-based land development models in practice, this thesis integrates dynamic traffic assignment (DTA) with agent-based positive travel behavior model as a feasible tool for land development and transportation policy analysis. The integrated model enhances the behavior realism of DTA as well as captures traffic dynamics. It provides a low-cost approach to conduct new traffic analysis which emphasis on not only regional/local system mobility, but also individual behaviors. A land development analysis and a flexible work schedule policy analysis are illustrated in this paper. Unlike traditional land development impact studies, a great deal of travel behavior shift is obtained via this integrated model, which creates a new way for land development and policy analysis.en_US
dc.identifierhttps://doi.org/10.13016/M2X035
dc.identifier.urihttp://hdl.handle.net/1903/16231
dc.language.isoenen_US
dc.subject.pqcontrolledTransportation planningen_US
dc.subject.pquncontrolledagent-based modelen_US
dc.subject.pquncontrolleddtaliteen_US
dc.subject.pquncontrolleddynamic traffic assignmenten_US
dc.subject.pquncontrolleddynusten_US
dc.subject.pquncontrolledpositive modelen_US
dc.subject.pquncontrolledtravel behavioren_US
dc.titleTraffic Analysis on Cumulative Land Development and Transportation Related Policy Scenariosen_US
dc.typeThesisen_US

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