Repository logo
 

Advancing medium- and heavy-duty electric vehicle adoption models with novel natural language processing metrics

Simple item page
dc.contributor.authorOuren, Fletcher, author
dc.contributor.authorBradley, Thomas H., advisor
dc.contributor.authorCoburn, Timothy, committee member
dc.contributor.authorWindom, Bret, committee member
dc.date.accessioned2024-05-27T10:31:57Z
dc.date.available2024-05-27T10:31:57Z
dc.date.issued2024
dc.description.abstractThe transportation sector must rapidly decarbonize to meet its emissions reduction targets. Medium- and heavy-duty decarbonization is lagging behind the light-duty industry 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 need higher participation rates and are difficult to generalize. To model fleet operators' decision-making priorities, this thesis applies a robust text analysis approach based on latent Dirichlet allocation and Bi-directional Encoder Representations of Transformers to two broad corpora of fleet adoption literature from academia and industry. Based on a newly developed metric, this thesis finds that the academic corpus emphasizes the importance of suitability, familiarity, norms, and brand image. These perception rankings are then passed to an agent-based model to determine how differences in perception affect adoption predictions. The results show a forecast of accelerated medium- and heavy-duty electric vehicle adoption when using the findings from the academic corpus versus the industry corpus.
dc.format.mediumborn digital
dc.format.mediummasters theses
dc.identifierOuren_colostate_0053N_18263.pdf
dc.identifier.urihttps://hdl.handle.net/10217/238394
dc.languageEnglish
dc.language.isoeng
dc.publisherColorado State University. Libraries
dc.relation.ispartof2020-
dc.rightsCopyright and other restrictions may apply. User is responsible for compliance with all applicable laws. For information about copyright law, please see https://libguides.colostate.edu/copyright.
dc.subjectBERTopic
dc.subjectlatent Dirichlet allocation
dc.subjectnatural language processing
dc.subjectelectrification
dc.subjectagent-based modeling
dc.subjectmedium- and heavy-duty transportation
dc.titleAdvancing medium- and heavy-duty electric vehicle adoption models with novel natural language processing metrics
dc.typeText
dcterms.rights.dplaThis Item is protected by copyright and/or related rights (https://rightsstatements.org/vocab/InC/1.0/). You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s).
thesis.degree.disciplineSystems Engineering
thesis.degree.grantorColorado State University
thesis.degree.levelMasters
thesis.degree.nameMaster of Science (M.S.)

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
Ouren_colostate_0053N_18263.pdf
Size:
1.04 MB
Format:
Adobe Portable Document Format
Download

Collections

2020-
Theses and Dissertations