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Electric vehicles (EVs) promise a sustainable solution to mitigating negative emission externalities of transportation systems caused by fossil-fueled conventional vehicles (CVs). While recent developments in battery technology and charging infrastructure can help evolve the niche market of EVs into the mass market, EVs are yet to be widely adopted by the public. This calls for an in-depth understanding of public adoption behavior of EVs as one dimension of vehicle decision making, which itself may be intertwined with other vehicle decision-making dimensions, especially vehicle transaction. This study presents an integrated choice model with latent variables (ICLV) to investigate households’—as a decision-making unit—decisions on vehicle transaction type (i.e., no transaction, sell, add, and trade) and vehicle fuel type (i.e., CVs and all EV types, including hybrid EV, plug-in hybrid EV, and battery EV) choice. To analyze the ICLV model empirically, one of the first revealed preferences national vehicle survey involving CVs and all EV types was conducted, which retrospectively inquired about 1,691 American households’ dynamics of vehicle decision making and demographic attributes over a 10-year period as well as their attitudes/preferences. The model estimation results highlight that EV adoption and vehicle transaction choice is influenced mainly by (1) the dynamics of household demographic attributes and (2) four latent constructs explaining attentiveness to vehicle attributes, social influence, environmental consciousness, and technology savviness. Notably, EV adoption promotion policies are found to be likely most effective on socially influenced individuals, who tend to consider advertisement and social trend more when making vehicle decisions.
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This content will become publicly available on August 1, 2027
Human-centered electric vehicle adoption framework for smart mobility: Modeling perceived range and charging anxiety as a psychological barrier
Electric vehicles (EVs) offer a transformative pathway toward reducing the environmental, economic, and health-related externalities of internal combustion engine vehicles in urban settings. Despite substantial advances in battery technology, charging infrastructure expansion, and supportive policy incentives, EV penetration remains limited which poses challenges for smart and sustainable mobility planning. A critical yet insufficiently modeled barrier to adoption lies in the psychological perceptions surrounding electric driving range and charging reliability, which is commonly framed as “range anxiety,” but more broadly reflecting perceived range and charging anxiety. To address this gap, this study introduces a latent psychological construct capturing individuals’ perceived range and charging anxiety and integrates it into a two-level nested logit (NL) model of vehicle transaction and fuel type choice. The model jointly represents (a) vehicle transaction decisions (keep, sell, trade, and add) and (b) fuel technology choices (conventional vehicle, hybrid EV, plug-in hybrid EV (PHEV), and battery EV (BEV)), thereby providing a behaviorally realistic representation of household vehicle dynamics. Bootstrap-based inference is employed to ensure robust estimation of both measurement and structural parameters. Using the sample dataset from the State of California, the results show that perceived range and charging anxiety significantly reduces the likelihood of adopting BEVs, particularly in fleet-expansion (add) decisions, while exerting no significant effect on PHEV adoption. This distinction suggests that PHEVs may function as transitional technologies for consumers concerned about electric mobility reliability. The findings highlight the importance of smart mobility strategies that address not only technological performance and infrastructure deployment, but also psychological perceptions shaping consumer behavior. By embedding latent behavioral factors into the vehicle choice process, the proposed framework advances citizen-centered mobility modeling and provides a robust decision-support tool for urban planners and transportation policymakers seeking to accelerate sustainable vehicle transitions.
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- Award ID(s):
- 2112650
- PAR ID:
- 10712718
- Publisher / Repository:
- Sage
- Date Published:
- Journal Name:
- Journal of Smart Cities and Society
- Volume:
- 5
- Issue:
- 3
- ISSN:
- 2772-3577
- Page Range / eLocation ID:
- 202 to 224
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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