Civil & Environmental Engineering Theses and Dissertations

Permanent URI for this collectionhttp://hdl.handle.net/1903/2753

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    EQUITY ISSUES IN ELECTRIC VEHICLE ADOPTION AND PLANNING FOR CHARGING INFRASTRUCTURE
    (2024) Ugwu, Nneoma; Niemeier, Deb; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    Electric Vehicles (EVs) offer a sustainable solution to fossil fuel dependency and environmentalpollution from conventional vehicles, crucial for mitigating climate change. However, low market penetration among minority and low-income communities raises equity and environmental justice concerns. This dissertation examines EV adoption and charging station access disparities in Maryland, focusing on sociodemographic factors such as race and income. To address the lack of minority representation in existing EV research surveys, we conducted anonline survey targeting people of color (POC) and low-to-moderate-income households. We received 542 complete responses. Ordinal regression models were used to analyze factors influencing EV interest. We then performed a cumulative accessibility study of EV infrastructure in Maryland. Pearson correlation analysis was used to show the relationship between charging station accessibility and sociodemographics. Population density showed a strong positive correlation (0.87) with charging deployment. We found that Baltimore City, had the highest population density and the highest concentration of EV charging in Maryland. We conducted a case study of Baltimore City’s EV infrastructure investments and policy efforts. Charging stations were categorized based on speed, network, access, and facility type. Spatial analysis andZero-Inflated Poisson (ZIP) regression models at the block group level were employed to investigate the disparities in EV charging infrastructure distribution within the City across minority and non-minority communities. Our findings show substantial disparities in EV perceptions between POC and Whitecommunities. The survey revealed that POC were more than twice more likely than White respondents to indicate that the availability of charging stations affects their interest in EV adoption, while the case studies revealed that POC populations are less likely to have access to EV infrastructure, necessitating targeted investment in charging options and subsidies in these communities. Our study also found the need for policies fostering residential charging station deployment, particularly in minority communities. To ensure equitable EV adoption, strategic investments in economically disadvantaged and rural areas beyond centralized regions are vital. This study informs evidence-based policies prioritizing accessibility, equity, and inclusivity in promoting a cleaner and sustainable transportation landscape.
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    PARAMETRIC AND NON-PARAMETRIC APPROACHES FOR THE PREDICTION OF THE DIFFUSION OF THE ELECTRIC VEHICLE
    (2020) Bas Vicente, Javier; Cirillo, Cinzia; Zofío Prieto, José Luis; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    Driven by environmental awareness and new regulations for fuel efficiency, electric vehicles (EVs) have significantly evolved in the last decade, yet their market share is still much lower than expected. In addition to understanding the reasons for this slow market penetration, it is crucial to have appropriate tools to correctly predict the diffusion of this innovative product. Recent works in forecasting the EV market combine substitution and diffusion models, where discrete choice specifications are used to address the former, and Bass-type to account for the latter. However, these methodologies are not dynamic and do not consider the fact that innovation occurs through social channels among members of a social system. This research presents two advanced methodologies that make use of real data to evaluate the adoption of the EVs in the State of Maryland. The first consists of a disaggregated substitution model that considers social influence and social conformity, which is then embedded in a diffusion model to predict electric vehicle sales. The second, in contrast, relies on non-parametric machine learning techniques for the classification of potential EV purchasers. Both make use of data collected through a stated choice experiment specifically designed to capture the inclination of users towards EVs.