Network modeling and optimization for energy and sustainable transit
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Energy and transportation systems are integral to our infrastructure. Along with other types of networks, critical challenges constantly arise, particularly with regard to accessibility, efficiency, optimality, and sustainability. In this dissertation, we use mixed integer programming, data mining and mixed complementarity techniques to address some of these challenges. We have developed an improved schematic mapping algorithm to facilitate the process of network representation for a variety of systems beyond transportation. We also discover fundamental patterns in bicycle ownership on a global scale with implications for sustainable urban planning and public health outcomes. Finally, we model the fast-growing crude oil market in North America, implementing scenarios that point to integrated approaches to exports, pipeline investments and targeted rail restrictions as most viable for addressing medium-term oil transportation concerns. The methods we employ are generalizable to other types of energy and transit systems, and beyond. Finally, we discuss the importance of these methods to newer applications.