The roles of users in electric, shared and automated mobility transitions
Open accessAbstract
This paper synthesizes insights from 19 peer-reviewed articles published in this Special Issue on the roles of users in electric, shared and automated mobility. While many researchers and stakeholders remain inspired by the potential low costs and societal benefits of these innovations, less is known about the real-world potential for uptake and usage. To better understand the likelihood and impacts of widespread uptake, we explore the perceptions of actual and potential users, including drivers, passengers, owners, and members, as well as other stakeholders such as pedestrians, planners, and policymakers. The Special Issue examines a range of cases, including plug-in electric vehicles, car-share and bike-share programs, ride-hailing and automated vehicles. For each innovation, we organize insights on user perceptions of benefits and drawbacks into four categories. Much of the research to date focuses on the first category, private-functional perceptions, mainly total cost of ownership (e.g., $/km), time use and comfort. Our synthesis however spans to the three other categories for each innovation: private-symbolic perceptions include the potential for social signaling and communicating identity; societal-functional perceptions include GHG emissions, public safety and noise; and societal-symbolic perceptions include inspiring pro-societal behavior in others, and the potential to combat or reinforce the status quo system of “automobility”. Further, our synthesis demonstrates how different theories and methods can be more or less equipped to “see” different perception categories. We also summarize findings regarding the characteristics of early users, as well as practical insights for strategies and policies seeking societally-beneficial outcomes from mass deployment of these innovations.
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Misaligned incentives in early stages of technological development could result in lock-in to a service with high GHG emissions.
“Misaligned incentives in the early stages of technological development could result in the lock-in to a service with high GHG emissions [36,44].”
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Tackling climate change with machine learning
Rolnick, D., Donti, P. L., Kaack, L. H., Kochanski, K., Lacoste, A., Sankaran, K., Ross, A. S., Milojevic-Dupont, N., Jaques, N., Waldman-Brown, A., et al. (2023) · ACM Computing Surveys · doi:10.1145/3485128 · Cited by 336 · see citing works
- Authors
- Axsen, J., & Sovacool, B. K.
- Affiliations
- Simon Fraser University (CA)
- University of Sussex (GB)
- Corresponding author
- Axsen, Jonn
- Venue
- Transportation Research Part D: Transport and Environment
- Publisher
- Elsevier BV
- Published
- 2019-06
- Volume
- 71
- Pages
- 1-21
- ISSN
- 1361-9209 (ISSN-L)
- OpenAlex
- W2935739837
- Cited by
- 183
- References
- 101
- Citations by year
- 2026: 19 · 2025: 18 · 2024: 15 · 2023: 20 · 2022: 37 · 2021: 38 · 2020: 28 · 2019: 8
- FWCI
- 12.66
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Transportation Research Part D Transport and Environment (publishedVersion, cc-by)
- Figshare (submittedVersion, cc-by)
- Sussex Research Online (University of Sussex) (submittedVersion, cc-by)
- Funders
- Pacific Institute for Climate Solutions
- Topics
- Physical Sciences › Engineering › Automotive Engineering › Transportation and Mobility Innovations
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Electric Vehicles and Infrastructure
- Social Sciences › Transportation › Transportation Planning and Optimization
- Keywords
- user perception, electric vehicles, shared mobility, autonomous vehicles, ride-hailing, car sharing, bike sharing, technology adoption, total cost of ownership, social signaling, identity, greenhouse gas emissions, public safety, noise, automobility, early adopters, transport policy, behavior change
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