Clean Transportation: Effects of Heterogeneity and Technological Progress on EV Costs and CO2 Abatement, and Assessment of Public EV Charging Stations

The advent of Electric Vehicles (EV) in the private transportation sector is viewed as a means of reducing emissions and making significant efforts towards reducing climate change impacts. However, when it comes to adopting and/or promoting a new technology through subsidies, the consumers’ needs are seldom given significant attention. Moreover, most analyses informing policy making assess the potential of new and cleaner technologies like EVs based on an average consumer’s needs and behavior. Given heterogeneity, these analyses miss subpopulations that benefit (or lose) more than an average consumer. In fact, private transportation greatly depends upon how the diversity of consumers choose to commute and what kind of vehicles they choose to possess. Especially in the United States of America (U.S.), each consumer faces different needs for their daily commute, which dictates their preferences for vehicles. This behavioral heterogeneity in addition to the geographic locations of consumers makes the U.S. private transportation sector an intricate system. The locations of the U.S. define fuel prices as well as emissions from electricity production. Therefore, these behavioral and geographic heterogeneities are highly crucial while calculating the benefits and potentials of EVs. The analyses conducted for this dissertation consider these heterogeneities to accommodate the nuances in consumers. This consideration of heterogeneities is the most critical aspect of this work. Chapter 2 of this dissertation builds a Marginal Abatement Cost Curve (MACC) for Electric Technology Vehicles (ETVs) which incorporates these heterogeneities, behavioral and geographical. With current gasoline and battery cell prices, result indicate that without federal tax credits, about 1.9% of the population would receive direct financial benefits from purchasing an ETV. This subpopulation drives over 4 times (over 48,000 miles annually) more than the average consumer (11,700 miles). The consideration of the heterogeneities has made it possible to recognize this subpopulation. The scenario analyses are conducted for different fuel and battery cell prices. These analyses shed light on how different subpopulations benefit financially and environmentally from ETVs. In this chapter, the impacts of federal tax credits with and without considering heterogeneities are estimated, suggesting why policy analyses need to incorporate consumer heterogeneities while assessing benefits of government subsidies. Given these results on economic and carbon benefits of ETVs, Chapter 3 builds an integrated model of adoption that includes endogenous technological progress—through learning rates—where due to initial adopters the technology is made cheaper for the future ones. The feedback loop developed in this chapter takes into consideration the cumulative production of the technology and estimates price reductions using learning rates. Reduced capital costs then propel more consumers to adopt ETVs making the technology cheaper, again increasing the consumer base that benefits from them. The economic benefits of buying an ETV versus a conventional one costs depend on battery costs, non-battery EV costs, and the future of conventional vehicles. Results are that the future market penetration (share of consumers economically benefitting) is sensitive to two poorly understood quantities: non-battery EV costs and cost increases in conventional vehicles driven by future emission standards. Federal tax credits are also studied in how they stimulate adoption and in turn technological progress of ETVs. Governments are not only investing in subsidies for consumer purchase of ETVs but also in installing public EV charging stations. These charging stations are expected to motivate consumers to choose ETVs over conventional vehicles and help reduce range-anxiety. In Chapter 4 an assessment is conducted to understand how these public resources are being used. Results reveal the behavior of consumers at the public EV charging stations using empirical data collected in the City of Rochester. A data distillation is first conducted for the raw data to construct the daily charging profiles of the EV users. A pattern analysis is then performed to identify 5 distinct and homogenous clusters of daily charging profiles of the consumers. This work defines the operational inefficiency of the public charging station as the time spent in parking without charging out of the total time a PEV user accessed the public charging station. This analysis uncovers a significant inefficient operation of these public EV charging stations, i.e. EVs remained parked at stations long after charging is finished. An estimation of the opportunity cost of reducing this observed inefficiency in terms of Greenhouse Gas emissions savings is also conducted in this chapter. The main policy takeaways of this dissertation are that identifying key subpopulations who benefit from the ETVs is highly significant and possible only by incorporating behavioral and geographical heterogeneities. This allows a more precise estimation of impacts of policies such as the federal tax credits. Secondly, the initial adopters make the technology cheaper for the latter adopters. However, the future market parity of ETVs with conventional vehicles depends on poorly understood factors such as current costs and learning rates of non-battery EV technologies and future cost increases in conventional vehicles driven by stricter emissions requirements. Lastly, the use of public resources, such as public charging stations needs to be studied. They are expensive to create, and inefficient use may deter possible EV adopters. Furthermore, the possible opportunity cost of reducing emissions by using the charging station more efficiently allows better use of a public resource

TRANSPORTATION RESILIENCE ARCHITECTURE: A FREMEWORK FOR ANALYSIS OF INFRASTRUCTURE, AGENCY AND USERS

How do some countries, or sectors of it, overcome potentially disastrous events while others fail at it? The answer lies on the concept of resilience, and its importance grows as our environment’s deterioration escalates, limiting the access to economic, social, and natural resources. This study evaluates resilience from a transportation perspective and defines it as “the ability for the system to maintain its demonstrated level of service or to restore itself to that level of service in a specified timeframe” (Heaslip, Louisell, & Collura, 2009). The literature shows that previous evaluation approaches usually do not directly integrate all perspectives of a transportation system. In this manner, this study introduces the concept of Transportation Resilience Architecture (TRA) as a framework for evaluating resilience of a transportation system through the cumulative effect of a system’s Infrastructure, Agency and User layer. This research introduces three quantitative methodologies as a way to evaluate resilience through TRA. For Infrastructure, a practical tool for measuring the level of accessibility to “safe zones” is presented, which takes advantage of the logsum measure resulting from Statewide Transportation Models. Results from the two locations analyzed (Frederick, MD and Anacostia, D.C.) suggest a positive correlation between income and accessibility. For Agency, metrics collected through a thorough literature review where combined with survey data to develop an evaluation framework based on Fuzzy Algorithms that yields to an index. The end product highlights the importance of interoperability as a disaster preparedness and response enhancing practice. Finally, for User, a dynamic discrete choice model was adapted to evaluate evacuation behavior, taking into account the disaster’s characteristics and the population’s expectations of them—a first from an evacuation perspective. The proposed framework is estimated using SP evacuation data collected on Louisiana residents. The result indicates that the dynamic discrete choice model excels in incorporating demographic information of respondents, a key input in policy evaluation, and yields significantly more accurate evacuation percentages per forecast

Market Penetration of New Vehicle Technology: A Generalized Dynamic Approach for Modeling Discrete-Continuous Decisions

Energy consumption and greenhouse gas (GHG) emissions are at their highest levels in history. One of the largest sources of GHG emissions in the United States is from burning fossil fuels for transportation. In developing countries GHG emissions from private vehicles are growing rapidly with their wealth. Government agencies attempt to reduce dependency on fossil fuels by regulating the ownership/usage of private vehicles, promoting vehicles with higher engine efficiency, introducing new fuel types, and defining stricter emission standards. Hybrid and electric vehicles are gaining consumers’ interest and trust, and their sale shares are gradually increasing. Meanwhile, environmental awareness, taxes on conventional gasoline cars, and incentives for cars with new technologies, make small and alternative-fuel vehicles more attractive. The future of personal transportation is uncertain; in particular, car ownership, vehicle type preferences and usage behavior are likely to change in surprising ways. In this context, it is important to assess the influence of the vehicle market evolution on consumer’s vehicle demands and travel behaviors. This dissertation proposes a comprehensive modeling framework that is able to analyze different dimensions of the car purchasing and usage problem. A multi-facet approach is taken for the investigation, and different model types are proposed. The investigation starts with a mixed logit model that accounts for time-series choices, heterogeneity in preferences and correlation across alternatives. This model is estimated on Stated Preference Survey data collected in Maryland and quantifies market elasticities and willingness-to-pays for improving car characteristics. Afterward, a dynamic discrete choice model is developed to predict the diffusion of hybrid and electric cars in Maryland, with consideration of household’s forward-looking behavior and stochasticity in vehicle market evolution. This model focuses on vehicle purchase time and vehicle type choice. To further consider vehicle usage decision, an integrated discrete-continuous choice model is proposed to jointly estimate household’s discrete choices on vehicle type/ownership and continuous choice on vehicle usage. The model is applied to estimate household-level vehicle emissions in Maryland, USA and Beijing, China. The dissertation concludes with a sequential discrete-continuous choice model. The modeling framework is applied to estimate vehicle ownership and usage decisions of forward-looking agents over time in a finite time horizon. In particular, a recursive probit model is formulated to estimate a sequence of vehicle holding decisions, while a regression is used to estimate a sequence of vehicle usage decisions. The proposed model is tested and validated on simulated discrete and continuous choices in a car ownership problem setting. The dissertation contributes to the theory of dynamic models for discrete-continuous decisions. The sequential discrete-continuous choice model is the first to measure the dynamic interdependency between discrete choice and continuous choice over time. The dissertation also contributes to the understanding of critical transportation issues, including market penetration of new vehicle technology, estimation of household-level vehicle emissions, and policy evaluation for promoting green vehicles and reducing dependency on cars and emissions

Understanding Car Ownership among Households in Developing Countries: A Case Study of Accra, Ghana

Household car ownership is a widely researched area due to the trade-offs between the benefits of the mobility provided by the car and the numerous negative impacts the car has on the environment. Most of the studies on car ownership have been conducted in developed countries, although more recently there are studies in emerging economies of the world. There are, however, very few studies on car ownership in developing countries, especially cities in Sub-Saharan Africa. The literature has some key commonalities as drivers to increased car ownership such as rising income and positive attitudes towards the car as a status symbol. There are, however, some important gaps with regards to understanding car ownership in the developing world. First, the household structures and social norms can sometimes be quite different. This may influence the propensity of different parts of society to want to own a car. Secondly, the context in which an ownership decision is being considered can be very different. In the case of developing country city like Accra, few have the opportunity to buy a new car with most being older imports and it may be that the issues such as status a car affords someone are different. Third, the context of public transport is very different. Levels of access to informal public transport could be so high generally that limited service provision does not offer the same explanatory power in understanding car ownership as witnessed in developed countries. The quality of the services and their informality may also be a factor in explaining the relative attractiveness of the car. More recent literature from developed countries is often looking to understand what might be effective in undoing mass car ownership whereas developing countries are trying to understand growth. The context of growth in developing countries is very different to that of the growth periods post the Second World War in the developed world and so new insights are required. This research seeks to bridge those gaps by understanding the factors that influence car ownership in a low car owning economy by researching on potential variables which are identified to affect car ownership. The research utilises both qualitative and quantitative methods. Using Accra, the capital of Ghana as a case study, a focus group discussion was undertaken to gain insight into the study area by understanding contextual issues to help in the development of questionnaires. Further to this, a household data collection was undertaken using questionnaires targeting specifically households in high-income communities followed by households in middle-income and low-income communities. In all 547 usable responses were obtained after the survey which provided data relating to household socio-demographic characteristics, trip characteristics, public transport accessibility and attitude towards car and public transport. The results from the research indicate strong influence of income and number of people employed within a household on car ownership. Other household characteristics like household size, type of household and number of children with household are identified not to be significant factors in understanding household car ownership. The research indicates that car is largely a utility purchase in the city of Accra indicating that life is difficult without owning a car. Also, whilst there exists universal coverage of the informal public transport which appears to be the dominant means of transport in the city there exist numerous negative attributes of the services they provide. Efforts to reduce the rate of car ownership will need to follow a twin track of significantly improving the quality of journeys on public transport along with restraining the use of cars to prevent the gridlock which will otherwise result as incomes grow