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Priority Setting and Integrated Assessment of Transportation System Vulnerabilities to Deep Uncertainties

You, Haowen
Thesis/Dissertation; Online
You, Haowen
Lambert, James
Deep uncertainty suggests situations in which the parties to a decision lack consensus on 1) the structural models to describe interactions among a system's variables, 2) the stochastic models to represent uncertainty about key parameters in the models, and 3) the methodologies to value the desirability of alternative outcomes. Deep uncertainties need to be addressed in strategic planning and priority-setting for transportation systems. In this context, traditional economic analysis and risk analysis of particular uncertainties can be prohibitive due to sparse data, complex models, and unforeseen interactions of climate change with other stressors. Nevertheless, decision-makers and planners need to assess the system vulnerability and allocate resources to address the range of deep uncertainties. Recent work in this topic has quantified the influence of deep uncertainties from a single management perspective, via expert elicitation to update the priorities of strategic resources for climate and non-climate stressors. There remains a need to address the multiple perspectives of priority-setting. For transportation agencies, the key perspectives include asset management, project selection, policy making, demographic/geographic equity, and others. This dissertation identifies and quantifies the influence of climate change combining with other sources of uncertainty to priority-setting in several perspectives. The approach adopts methods of multiple criteria decision analysis and scenario-based planning that have been used by agencies for long-range transportation plans. The approach is demonstrated with the Hampton Roads region of the Commonwealth of Virginia, USA. The results include that climate change combining with increased travel demand is relatively influential to priority-setting across scenarios that include economic downturn, ecological change, wear and tear, and climate change alone. The influential scenarios are shown to differ by perspective.
University of Virginia, Department of Systems Engineering, PHD, 2013
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Libra ETD Repository
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