In many countries across the world, fossil fuels, especially petroleum, are the largest energy source for powering the socio-economic system and the transportation sector dominates the consumption of petroleum in these societies. As the petroleum price continuously climbs and the threat of global climate changes becomes more evident, the world is now facing critical challenges in reducing petroleum consumption and exploiting alternative energy sources. A massive adoption of plug-in electric vehicles (PEVs), especially battery electric vehicles (BEVs), offers a very promising approach to change the current energy consumption structure and diminish greenhouse gas emissions and other pollutants. Understanding how individual electric vehicle drivers behave subject to the technological restrictions and infrastructure availability and estimating the resulting aggregate supply-demand effects on urban transportation systems is not only critical to transportation infrastructure development, but also has determinant implications in environment and energy policy enactment. Driving PEVs inevitably changes individual’s travel and activity behaviors and calls for fundamental changes to the existing transportation network and travel demand modeling paradigms to accommodate changing cost structures, technological restrictions, and supply infrastructures. A prominent phenomenon is that all PEV drivers face a distance constraint on their driving range, given the unsatisfactory battery-charging efficiency and scarce battery-charging infrastructures in a long period of the foreseeable future. Incorporating this distance constraint and the resulting behavioral changes into transportation network equilibrium and travel demand models (static and/or dynamic) raises a series of important research questions. This dissertation focuses on analyzing the impact of a massive adoption of BEVs on urban transportation network flows. BEVs are entirely dependent on electricity and cannot go further once the battery is depleted. As a modeling requirement in its simplest form, a distance constraint should be imposed when analyzing and modeling individual behaviors and network congestions. With adding this simple constraint, this research work conceptualizes, formulates and solves mathematical programming models for a set of new BEV-based network routing and equilibrium problems. It is anticipated that the developed models and methods can be extensively used in a systematic way to analyze and evaluate a variety of system planning and policy scenarios in decision-making circumstances of BEV-related technology adoption and infrastructure development. / text

京都大学 / 0048 / 新制・課程博士 / 博士(工学) / 甲第18568号 / 工博第3929号 / 新制||工||1604(附属図書館) / 31468 / 京都大学大学院工学研究科都市社会工学専攻 / (主査)教授 谷口 栄一, 准教授 宇野 伸宏, 准教授 山田 忠史 / 学位規則第4条第1項該当 / Doctor of Philosophy (Engineering) / Kyoto University / DGAM

Many empirical and theoretical studies show that workers obtain jobs through their social contacts. This paper attempts to investigate how the difference of individuals’ intrinsic abilities in obtaining their jobs affects the social network structure that emerges. When the probability of players who get job information by themselves is large enough, low-ability players will maintain more contacts than high-ability players do. We analyze the equilibrium network structures in homogeneous society and heterogeneous society respectively. For analyzing more complex cases that people are heterogeneous in their ability of obtaining jobs, we suggest a new equilibrium concept: Forward Induction Network Equilibrium (FINE), a refinement of pairwise stability equilibrium. According to FINE, the possible equilibrium network structures can be reduced drastically and the outcomes are either symmetric or close-to-symmetric equilibrium networks. We show that the difference of social contacts among overall individuals is no greater than one contact in the close-to-symmetric equilibrium networks.