<p> Intelligent transportation systems use in-pavement inductive loop sensors to collect real time traffic data. This method is very expensive in terms of installation and maintenance. Our research is focused on developing advanced algorithms capable of generating high amounts of energy that can charge a battery. This electromechanical energy conversion is an optimal way of energy scavenging that makes use of piezoelectric sensors. The power generated is sufficient to run the vehicle detection module that has several sensors embedded together. To achieve these goals, we have developed a simulation module using software&rsquo;s like LabVIEW and Multisim. The simulation module recreates a practical scenario that takes into consideration vehicle weight, speed, wheel width and frequency of the traffic.</p>

<p> The substitution of electrical energy for gasoline as a transportation fuel is an initiative both with a long history, and one made both pressing and important in today's policy discussion by renewed interest in plug-in vehicles. The research presented in this dissertation attempts to inform the policy discussion for governments, for electric utilities, for the makers of electric cars, and for the industries developing and planning charging infrastructure. To that end, the impacts of variations to several possible system design parameters, on several metrics of evaluation, are assessed. The analysis is based on a dataset of vehicle trips collected by Georgia Institute of Technology, tracking almost 500 vehicles that commute to, from or within the Atlanta city center, comprising Atlanta `commuter-shed'. By assuming that this dataset of trips defines the desired travel behavior of urban and suburban American populations, the effects of travel electrification in personal vehicles can be assessed. </p><p> Several significant and novel findings have emerged from this research. These include the conclusion that at-work charging is not necessarily the logical next step beyond home-charging, as it will in general add little to the substitutability of electric vehicles. In contrast, high power en-route charging, combined with modest power home charging is shown to be surprisingly effective, potentially requiring of EV drivers a total time spent at en-route recharging stations similar to that for liquid fueled cars. From the vehicle marketing perspective, a quantification of the hybrid household effect, wherein multi-vehicle households own one EV, showed that about a quarter of all households could adopt a vehicle with 80 miles of range with no changes to travel patterns. Of interest to grid management, this research showed an apparent maximum fleet-wide load from unregulated charging of about 1 kW per vehicle, regardless of EVSE power or EV battery size. This contrasts with a potential late night load spike an order of magnitude higher under certain time-of-use charging algorithm implementations. Finally, an EVSE and EV power capacity of 10-12 kW was shown to be a likely optimum if grid services from modulated charging are being considered.</p>

Typescript (photocopy). / Digitized by Kansas Correctional Industries

Transportation Energy Analysis for Single-Family Residential Construction in California Tyler Langley Since the oil crisis of 1973, energy use in the United States of America has been a growing area of concern. Studies have shown that the construction industry is responsible for almost half of all annual energy consumption. With this awareness, the analysis of energy use within the related construction fields has become an emergent subject. One facet of construction energy use that has been less studied than others is that of the energy consumed in transporting building materials from manufacturing plants to construction sites. This thesis proposes a methodology for determining the energy consumed during the transportation of building materials to a construction site and applies this methodology to estimate the transportation component of the total energy consumed in the lifecycle of a residential building in California. Comparisons are then drawn among the embodied energy of the materials used in the construction of the building, the energy used to transport the materials and the products used in the on-site assembly of the building, and the energy consumed during the occupancy of the building. The first chapter covers the intent of the thesis, as well as a categorization and explanation of the main areas of energy usage in the construction industry. This is followed by a delineation of the methodology used to research transportation energy. Chapter 2 details the development of the framework that is discussed in Chapter 1. This includes the unique problem areas of calculating transportation energy, the resulting parameters that focus the area of study, and the general assumptions derived from those parameters. Chapter 3 is a case study of a single-family two-story house in northern California. First, the considerations and reasons for the choice are defined, establishing this as a representative residence for the area. The material choices and structural system choices are also discussed. Then, the framework introduced in Chapter 2 is applied in the case study. This introduces more case-specific problems in the types of calculations used for estimating transportation energy. Chapter 4 contains a summary of the findings as well as a reflection on the process followed by suggestions for future research and application for the subject of transportation energy usage. In this summary, it is shown that the energy used in transportation of materials to the site of the case study house amounts to 10.5 million Btu, which is roughly 2.5% of the embodied energy, and 21% of the occupational energy usage per year.

Since the early 1970's, fuel consumption in the transportation sector has been one of the major issues facing planners trying to conserve energy. Generally, fuel consumption is influenced directly by the number of vehicles, the distance travelled, the operating speed and the overall population of city. Traffic engineers spent a great deal of time solving such a problem sometimes by introducing the concept of traffic management, the different actions and strategies that reduce the fuel consumption and some other times by estimating the energy saving to evaluate the effectiveness of these actions. Despite best efforts, energy savings estimation have shown wide fluctuations. This study provides a preliminary Investigation of the impact of city size, in the medium range, on potential energy savings accrued due to implementation of Transportation Energy Management Measures (TEMM's). The data sources used includes the energy savings for each of five city size groups, subjected to 23 TEMM's. These data were rearranged and regressed over city size by using the CURFIT technique. Formulae were derived for each of the TEMM's. For the purpose of verification, the Community Benefits Analysis program (CBA) was applied to test some of the obtained results. It is thus concluded that the resulting energy savings, using the regressed equations provide a reasonable way to predict potential benefits across the medium range of city sizes. / Thesis / Master of Engineering (ME)

京都大学 / 0048 / 新制・課程博士 / 博士(工学) / 甲第17878号 / 工博第3787号 / 新制||工||1579(附属図書館) / 30698 / 京都大学大学院工学研究科都市社会工学専攻 / (主査)教授 中川 大, 教授 谷口 栄一, 准教授 松中 亮治 / 学位規則第4条第1項該当 / Doctor of Philosophy (Engineering) / Kyoto University / DFAM

published_or_final_version / Civil and Structural Engineering / Doctoral / Doctor of Philosophy

There is much potential to decrease energy consumption in the U.S. by encouraging compact, centralized development. Although many studies have examined the extent to which built environment and demographic factors are related to household energy use, few have considered both building and transportation energy together. We hypothesized that residents living further from city centers, or urban cores, consume more energy for both purposes than their inner city counterparts, resulting in a direct relationship between building and transportation energy usage. This hypothesis was tested with two case studies. The first focused on New York City. Annual building energy per unit of parcels, or tax lots, containing large multi-family structures was compared to the daily transportation energy use per household of traffic analysis zones (TAZs) (estimated with a regional travel demand model). Transportation energy showed a strong spatial pattern, with distance to urban core explaining 63% of variation in consumption. Building energy use was randomly distributed, resulting in a weak negative correlation with transportation energy. However, both correlation with distance to urban core and transportation energy became significant and positive when portion of detached single-family units for TAZs was used as a proxy for building energy. Structural equation models (SEMs) revealed a direct relationship between log lot depth and both uses of energy, and inverse relationship between portion of attached housing units and transportation energy. This supports the notion that sprawling development increases both the building and transportation energy consumption of households. For the second analysis, annual building and automobile energy use per household were estimated for block groups across the 50 most populous U.S. metropolitan regions with Esri Consumer Expenditure Data. Both forms of energy consumption per household were lowest in inner cities and increased at greater distances from urban cores. Although there may be some error in estimates from modeled expenditure data, characteristics associated with lower energy use, such as portion of attached housing units and commuters that utilize transit or pedestrian modes, were negatively correlated with distance to urban core. Overall, this work suggests there are spatial patterns to household energy consumption, with households further from urban cores using more building and transportation energy. There is the greatest gain in efficiency to be had by suburban residents.

Changes in climate caused by changes in anthropogenic (i.e. "man-made") greenhouse gas (GHG) emissions have become a major public policy issue in countries all over the world. With an estimated 28.4% of these emissions attributed to the transportation sector, attention is being focused on strategies aimed at reducing transportation GHG emissions. Quantifying the change in GHG emissions due to such strategies is one of the most challenging aspects of integrating GHG emissions and climate change into transportation planning and policy analysis; the inventory techniques and methods for estimating the impact of different strategies and policies are still relatively unsophisticated. This research developed a method for estimating intercity passenger transportation energy and carbon footprints and applied this method to three US DOT-designated high speed rail (HSR) corridors in the U.S.-- San Francisco/Los Angeles/San Diego; Seattle/Portland/Eugene, and Philadelphia/Harrisburg/Pittsburg. The methodology consists of estimating the number of trips by mode, estimating the direct CO₂ emissions, and estimating indirect CO₂ emissions. For each study corridor the impacts of different strategies and policies on carbon dioxide emissions were estimated as an illustration of the policy application of the developed methodology. The largest gain in CO₂ savings can be achieved by strategies aiming at automobile emissions, due to its sizeable share as main mode and access/egress mode to and from airports and bus and train stations: an average fuel economy of 35.5 mpg would result in a 38-42% savings of total CO₂ emissions; replacing 25% of gasoline use with cellulosic ethanol can have a positive impact on CO₂ emissions of about 13.4-14.5%; and a 10% market share for electric vehicles would result in potential CO₂ savings of 3.4-7.8%. The impact of a 20% or 35% improvement in aircraft efficiency on CO₂ savings is much lower (0.88-3.65%) than the potential impacts of the policies targeting automobile emissions. Three HSR options were analyzed using Volpe's long-distance demand model: HSR125, HSR150, and HSR200. Only the HSR150 and HSR200 would result in CO₂ savings, and then just for two of the three corridors: the Pacific Northwest (1.5%) and California (0.8-0.9%). With increased frequency and load factors, a HSR150 system could result in CO₂ savings of 5.2% and 1.8% for the Pacific Northwest and California, respectively. This would require a mode shift from auto of 5-6%. This shift in auto mode share would mainly have to be a result of pricing strategies. From these results, HSR may not be such an obvious choice, however, with increased ridership and diversions from other modes, CO₂ savings increase significantly due to the lower emissions per passenger mile for HSR. The framework developed in this study has the ability to determine the GHG emissions for such HSR options and increased diversions.