Integrated network-based models for evaluating and optimizing the impact of electric vehicles on the transportation system

dc.contributor.advisorWaller, S. Travisen
dc.contributor.committeeMemberBoyles, Stephen D.en
dc.contributor.committeeMemberMachemehl, Randy B.en
dc.contributor.committeeMemberZhang, Zhanminen
dc.contributor.committeeMemberKendrick, David A.en
dc.contributor.committeeMemberHasenbein, Johnen
dc.creatorZhang, Tien
dc.date.accessioned2012-11-13T15:48:26Zen
dc.date.accessioned2017-05-11T22:29:40Z
dc.date.available2012-11-13T15:48:26Zen
dc.date.available2017-05-11T22:29:40Z
dc.date.issued2012-08en
dc.date.submittedAugust 2012en
dc.date.updated2012-11-13T15:48:43Zen
dc.descriptiontexten
dc.description.abstractThe adoption of plug-in electric vehicles (PEV) requires research for models and algorithms tracing the vehicle assignment incorporating PEVs in the transportation network so that the traffic pattern can be more precisely and accurately predicted. To attain this goal, this dissertation is concerned with developing new formulations for modeling travelling behavior of electric vehicle drivers in a mixed flow traffic network environment. Much of the work in this dissertation is motivated by the special features of PEVs (such as range limitation, requirement of long electricity-recharging time, etc.), and the lack of tools of understanding PEV drivers traveling behavior and learning the impacts of charging infrastructure supply and policy on the network traffic pattern. The essential issues addressed in this dissertation are: (1) modeling the spatial choice behavior of electric vehicle drivers and analyzing the impacts from electricity-charging speed and price; (2) modeling the temporal and spatial choices behavior of electric vehicle drivers and analyzing the impacts of electric vehicle range and penetration rate; (3) and designing the optimal charging infrastructure investments and policy in the perspective of revenue management. Stochastic traffic assignment that can take into account for charging cost and charging time is first examined. Further, a quasi-dynamic stochastic user equilibrium model for combined choices of departure time, duration of stay and route, which integrates the nested-Logit discrete choice model, is formulated as a variational inequality problem. An extension from this equilibrium model is the network design model to determine an optimal charging infrastructure capacity and pricing. The objective is to maximize revenue subject to equilibrium constraints that explicitly consider the electric vehicle drivers’ combined choices behavior. The proposed models and algorithms are tested on small to middle size transportation networks. Extensive numerical experiments are conducted to assess the performance of the models. The research results contain the author’s initiative insights of network equilibrium models accounting for PEVs impacted by different scenarios of charging infrastructure supply, electric vehicles characteristics and penetration rates. The analytical tools developed in this dissertation, and the resulting insights obtained should offer an important first step to areas of travel demand modeling and policy making incorporating PEVs.en
dc.description.departmentCivil, Architectural, and Environmental Engineeringen
dc.format.mimetypeapplication/pdfen
dc.identifier.slug2152/ETD-UT-2012-08-5960en
dc.identifier.urihttp://hdl.handle.net/2152/ETD-UT-2012-08-5960en
dc.language.isoengen
dc.subjectElectric vehiclesen
dc.subjectIntegrated modelen
dc.subjectStochastic traffic assignmenten
dc.subjectNetwork designen
dc.subjectPricing and capacity designen
dc.subjectVariational inequalityen
dc.subjectSensitivity analysis based optimizationen
dc.titleIntegrated network-based models for evaluating and optimizing the impact of electric vehicles on the transportation systemen
dc.type.genrethesisen

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