Version 1
: Received: 10 May 2024 / Approved: 14 May 2024 / Online: 14 May 2024 (13:12:38 CEST)
How to cite:
Chowdhury, V.; Mitra, S. K.; Hernandez, S. Unraveling Electric Vehicle Preference: A Machine Learning Analysis of Vehicle Choice in Multi-Vehicle Households in the United States. Preprints2024, 2024050945. https://doi.org/10.20944/preprints202405.0945.v1
Chowdhury, V.; Mitra, S. K.; Hernandez, S. Unraveling Electric Vehicle Preference: A Machine Learning Analysis of Vehicle Choice in Multi-Vehicle Households in the United States. Preprints 2024, 2024050945. https://doi.org/10.20944/preprints202405.0945.v1
Chowdhury, V.; Mitra, S. K.; Hernandez, S. Unraveling Electric Vehicle Preference: A Machine Learning Analysis of Vehicle Choice in Multi-Vehicle Households in the United States. Preprints2024, 2024050945. https://doi.org/10.20944/preprints202405.0945.v1
APA Style
Chowdhury, V., Mitra, S. K., & Hernandez, S. (2024). Unraveling Electric Vehicle Preference: A Machine Learning Analysis of Vehicle Choice in Multi-Vehicle Households in the United States. Preprints. https://doi.org/10.20944/preprints202405.0945.v1
Chicago/Turabian Style
Chowdhury, V., Suman Kumar Mitra and Sarah Hernandez. 2024 "Unraveling Electric Vehicle Preference: A Machine Learning Analysis of Vehicle Choice in Multi-Vehicle Households in the United States" Preprints. https://doi.org/10.20944/preprints202405.0945.v1
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
Subject
Engineering, Transportation Science and Technology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
