Electric Vehicle Usage Patterns in Multi-Vehicle Households in the US: A Machine Learning Study

Vuban Chowdhury, Suman Kumar Mitra () and Sarah Hernandez
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Vuban Chowdhury: Department of Civil Engineering, University of Arkansas, Fayetteville, AR 72701, USA
Suman Kumar Mitra: Department of Civil Engineering, University of Arkansas, Fayetteville, AR 72701, USA
Sarah Hernandez: Department of Civil Engineering, University of Arkansas, Fayetteville, AR 72701, USA

Sustainability, 2024, vol. 16, issue 12, 1-21

Abstract: Electric vehicles (EVs) play a significant role in reducing carbon emissions. In the US, EVs are mostly owned by multi-vehicle households, and their usage is primarily studied in the context of vehicle miles traveled. This study takes a unique approach by analyzing EV usage through the lens of vehicle choice (between EVs and internal combustion engine vehicles) within multi-vehicle households. A two-step machine-learning framework (clustering and decision trees) is proposed. The framework determines the preferred trip category for EV use and captures the effects of household attributes, driver attributes, built-environment factors, and gas prices on EV use in multi-vehicle households. Results indicate that discretionary trips (accumulated local effect = 0.037) are mostly preferred for EV use. EV preference is more pronounced among households with fewer workers (<2) and lower income levels. These findings are valuable for policymakers and auto manufacturers in targeting specific market segments and promoting EV adoption.

Keywords: electric vehicles; multi-vehicle household; machine learning; clustering; decision tree; NHTS (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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Citations: View citations in EconPapers (1)

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