An interpretable machine learning approach to understanding the impacts of attitudinal and ridesourcing factors on electric vehicle adoption
Date
2021Affiliation
Universidad de Alcalá. Departamento de Economía.Bibliographic citation
BAS, J, ZOU, Z. & CIRILLO, C. An interpretable machine learning approach to understanding the impacts of attitudinal and
ridesourcing factors on electric vehicle adoption. Transportation Letters. 2021. ISSN 1942-7875.
Keywords
Electric vehicles
Attitudes
Ridesourcing
Machine learning
Local interpretable model-agnostic explanations (lime)
Description / Notes
13 p.
Document type
info:eu-repo/semantics/article
Version
info:eu-repo/semantics/publishedVersion
Rights
Attribution-NonCommercial-NoDerivatives 4.0 Internacional
Access rights
info:eu-repo/semantics/openAccess
Abstract
The global electric vehicle (EV) market has been experiencing an impressive growth in recent times.
Understanding consumer preferences on this cleaner, more eco-friendly mobility option could help guide
public policy toward accelerating EV adoption and sustainable transportation systems. Previous studies
suggest the strong influence of individual and external factors on EV adoption decisions. In this study, we
apply machine learning techniques on EV stated preference survey data to predict EV adoption using
attitudinal factors, ridesourcing factors (e.g., frequency of Uber/Lyft rides), as well as underlying sociodemographic and vehicle factors. To overcome machine learning models’ low interpretability, we adopt the innovative Local Interpretable Model-Agnostic Explanations (LIME) method to elaborate each factor’s contribution to the predicting outcomes. Besides what was found in previous EV preference literature, we find that the frequent usage of ridesourcing, knowledge about EVs, and awareness of environmental protection are important factors in explaining high willingness of adopting EVs.
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interpretable_bas_TL_2021.pdf | 483.5Kb |
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Files | Size | Format |
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interpretable_bas_TL_2021.pdf | 483.5Kb |
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