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Developing a profile of medium- and heavy-duty electric vehicle fleet adopters with text mining and machine learning.

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Title: Developing a profile of medium- and heavy-duty electric vehicle fleet adopters with text mining and machine learning.
Authors: Ouren, Fletcher1 (AUTHOR) fouren@colostate.edu, Trinko, David1 (AUTHOR) David.Trinko@colostate.edu, Coburn, Timothy1 (AUTHOR) Tim.Coburn@colostate.edu, Simske, Steven1 (AUTHOR) Steve.simske@colostate.edu, Bradley, Thomas H.1 (AUTHOR) Thomas.Bradley@colostate.edu
Source: Renewable Energy Focus. Sep2023, Vol. 46, p303-312. 10p.
Subject Terms: *Electric vehicles, *Greenhouse gas mitigation, *Electric vehicle batteries, Machine learning, Text mining
Abstract: The transportation sector must rapidly decarbonize to meet its emissions reduction targets. Medium- and heavy-duty decarbonization is lagging the light-duty sector due to technical and operational challenges and the choices made by medium- and heavy-duty fleet operators. Research investigating the procurement considerations of fleets has relied heavily on interviews and surveys, but many of these studies suffer low rates of participation and are difficult to generalize. To model fleet operators' decision-making priorities, we apply a robust text analysis approach based on latent Dirichlet allocation to a broad corpus of fleet adoption studies. We find operational compatibility to be the most salient factor, followed by long-term economics, technological familiarity, and perceived reliability. [ABSTRACT FROM AUTHOR]
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Database: GreenFILE
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Abstract:The transportation sector must rapidly decarbonize to meet its emissions reduction targets. Medium- and heavy-duty decarbonization is lagging the light-duty sector due to technical and operational challenges and the choices made by medium- and heavy-duty fleet operators. Research investigating the procurement considerations of fleets has relied heavily on interviews and surveys, but many of these studies suffer low rates of participation and are difficult to generalize. To model fleet operators' decision-making priorities, we apply a robust text analysis approach based on latent Dirichlet allocation to a broad corpus of fleet adoption studies. We find operational compatibility to be the most salient factor, followed by long-term economics, technological familiarity, and perceived reliability. [ABSTRACT FROM AUTHOR]
ISSN:17550084
DOI:10.1016/j.ref.2023.07.004