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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Vuban Chowdhury & Suman Kumar Mitra & Sarah Hernandez, 2024. "Electric Vehicle Usage Patterns in Multi-Vehicle Households in the US: A Machine Learning Study," Sustainability, MDPI, vol. 16(12), pages 1-21, June.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:12:p:5200-:d:1417577
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    References listed on IDEAS

    as
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    Full references (including those not matched with items on IDEAS)

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