EXPLORING ELECTRIC VEHICLE ADOPTION TRENDS USING ARTIFICIAL INTELLIGENCE
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Authors
Nguyen, Dinh Cuong (Ernest)
Tenney, Dan (Advisor)
McAdams III, Arthur C. (Advisor)
Issue Date
2024-04-05
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Abstract
Electric vehicles (EVs) have emerged as a promising alternative to traditional internal combustion engine vehicles (Muratori et al., 2021). Understanding the patterns and trends in EV adoption is crucial for policymakers, manufacturers, and consumers alike (Lee & Brown, 2021). In this research, artificial intelligence (AI) techniques are employed, specifically the Random Forest (RF) algorithm, to explore EV adoption trends over time. With a dataset containing nearly 900,000 instances of EV title and registration activity, the research identifies key factors influencing EV adoption in relation to vehicle primary use. This refers to the main purpose for which an EV is used, such as passenger, commercial, taxis, trucks, farm vehicles, and others. The study uncovers notable trends in EV adoption, including increasing transaction volumes over time, varying preferences for model years, and the impact of factors such as electric range and vehicle make on adoption patterns. These finding contribute to a deeper understanding the evolving EV market and inform strategies for sustainable transportation initiatives.
Description
UB Rise 2024, Department of Technology Management, School of Engineering