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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]
Copyright of Renewable Energy Focus is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: GreenFILE
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PubType: Academic Journal
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  Data: Developing a profile of medium- and heavy-duty electric vehicle fleet adopters with text mining and machine learning.
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  Data: <searchLink fieldCode="AR" term="%22Ouren%2C+Fletcher%22">Ouren, Fletcher</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> fouren@colostate.edu</i><br /><searchLink fieldCode="AR" term="%22Trinko%2C+David%22">Trinko, David</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> David.Trinko@colostate.edu</i><br /><searchLink fieldCode="AR" term="%22Coburn%2C+Timothy%22">Coburn, Timothy</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> Tim.Coburn@colostate.edu</i><br /><searchLink fieldCode="AR" term="%22Simske%2C+Steven%22">Simske, Steven</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> Steve.simske@colostate.edu</i><br /><searchLink fieldCode="AR" term="%22Bradley%2C+Thomas+H%2E%22">Bradley, Thomas H.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> Thomas.Bradley@colostate.edu</i>
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  Data: <searchLink fieldCode="JN" term="%22Renewable+Energy+Focus%22">Renewable Energy Focus</searchLink>. Sep2023, Vol. 46, p303-312. 10p.
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  Data: *<searchLink fieldCode="DE" term="%22Electric+vehicles%22">Electric vehicles</searchLink><br />*<searchLink fieldCode="DE" term="%22Greenhouse+gas+mitigation%22">Greenhouse gas mitigation</searchLink><br />*<searchLink fieldCode="DE" term="%22Electric+vehicle+batteries%22">Electric vehicle batteries</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Text+mining%22">Text mining</searchLink>
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  Data: 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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  Data: <i>Copyright of Renewable Energy Focus is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1016/j.ref.2023.07.004
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        Text: English
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        Type: general
      – SubjectFull: Greenhouse gas mitigation
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      – SubjectFull: Electric vehicle batteries
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      – SubjectFull: Machine learning
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      – SubjectFull: Text mining
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              Text: Sep2023
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