Date of Award:

8-2024

Document Type:

Dissertation

Degree Name:

Master of Science (MS)

Department:

Computer Science

Committee Chair(s)

Steve Petruzza

Committee

Steve Petruzza

Committee

Mario Harper

Committee

John Edwards

Abstract

As urban areas continue to grow, the deployment of electric vehicle (EV) charging infrastructure becomes crucial for sustainable development. This study is focused on the development of a data visualization tool that integrates diverse datasets, including traffic patterns, Points of Interest (POI), pollution levels, and socioeconomic indicators, to analyze the current state and potential expansion of EV charging stations. Our visualization tool highlights the significant impact of EV infrastructure on reducing urban pollution and improving socioeconomic outcomes. Areas with a higher density of charging stations show significantly lower levels of unemployment and pollution, emphasizing the dual benefits of EV adoption. Additionally, our tool visualizes vehicle mix on urban pollution, marking specific vehicle types as most impactful for electrification efforts by road segment and community. Our tool helps inform and support equitable and effective decisions in planning and implementing EV infrastructure, ensuring that the benefits are evenly distributed across all urban populations.

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