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dc.contributor.advisorWaller, S. Travisen
dc.creatorRuiz Juri, Nataliaen
dc.date.accessioned2009-10-22T14:20:58Zen
dc.date.available2009-10-22T14:20:58Zen
dc.date.issued2009-08en
dc.identifier.urihttp://hdl.handle.net/2152/6600en
dc.descriptiontexten
dc.description.abstractTraffic information, now available through a number of different sources, is re-shaping the way planners, operators and users think about the transportation network. It provides a powerful tool to mitigate the negative impacts of uncertainty, and an invaluable resource to manage and operate the network in real-time. More information also invites to think about traditional transportation problems from a different perspective, searching for a better utilization of the improved knowledge of the network state. This dissertation is concerned with modeling and evaluating the system-level impacts of providing information to network users, assuming that the data is utilized to guide an Adaptive System-Optimum (ASO) routing behavior. Within this context, it studies the optimal deployment of sensors for the support of ASO strategies, and it introduces a novel SO assignment approach, the Information-Based System Optimum (IBSO) assignment paradigm. The proposed sensor deployment model explicitly captures the impact of sensors' location on the expected cost of ASO assignment strategies. Under such strategies, a-priori routing decisions may be adjusted based on real-time information. The IBSO assignment paradigm leads to optimal flow patterns which take into account the ability of vehicles to collect information as they travel. The approach regards a subset of the system's assets as probes, which may face higher expected costs than regular vehicles in the search for information. The collected data is utilized to adjust routing decisions in real time, improving the expected system performance. The proposed problem captures the system-level impact of adaptive route choices on stochastic networks. The models developed in this work are rigorously formulated, and their properties analyzed to support the generation of specialized solution methodologies based on state-space partitioning and Tabu Search principles. Solution techniques are tested under a variety of scenarios, and implemented to the solution of several case studies. The magnitude and nature of the information impacts observed in this study illustrate problem characteristics with important theoretical, methodological and practical implications. The findings presented in this dissertation allow envisioning a number of practical applications which may promote a more efficient utilization of novel sensing and communication technologies, allowing the full realization of their potential.en
dc.format.mediumelectronicen
dc.language.isoengen
dc.rightsCopyright is held by the author. Presentation of this material on the Libraries' web site by University Libraries, The University of Texas at Austin was made possible under a limited license grant from the author who has retained all copyrights in the works.en
dc.subjectTransportation networken
dc.subjectAdaptive system-optimum strategiesen
dc.subjectInformation impacten
dc.titleModeling the system-level impacts of information provision in transportation networks : an adaptive system-optimum approachen
dc.description.departmentCivil, Architectural, and Environmental Engineeringen
thesis.degree.departmentCivil, Architectural, and Environmental Engineeringen
thesis.degree.disciplineCivil Engineeringen
thesis.degree.grantorThe University of Texas at Austinen
thesis.degree.levelDoctoralen
thesis.degree.nameDoctor of Philosophyen


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