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Published VersionCC BY V4.0, Open Access
Journal article
Understanding Shared Autonomous Vehicle Preferences: A Comparison between Shuttles, Buses, Ridesharing and Taxis
Sustainability , Vol.14(20), pp.1-14
2022
Abstract
Shared autonomous vehicles (AVs) will soon be introduced in public transportation as cities and their transportation systems become 'smarter'. This brings long-term environmental, economic and societal benefits to cities. However, shared AVs will not only need to overcome technological challenges but also prevail against social barriers for successful marketplace penetration. Hence, we proposed and investigated the acceptance of four shared AV service designs for public use in this study, namely, autonomous buses, shuttles, AV rideshares and autonomous or robo-taxis. An online survey conducted in Singapore with 734 adults found the greatest receptiveness toward the introduction of autonomous shuttles, in part due to perceptions that they will perform well and be easy to adopt. This aligns with ongoing shared AV trials where AV shuttles are mostly used. Larger autonomous buses had the second-highest acceptance. AV rideshares and taxis seem to largely appeal to the existing regular users of the conventional counterparts of these services. These results suggest that to encourage a mode switch from public transport to ridesharing and taxis, or vice versa, shared AVs need to appeal to users beyond being an automated version of existing modes. That is, shared AVs need to address an underserved or unmet transportation need or population.
Details
- Title
- Understanding Shared Autonomous Vehicle Preferences: A Comparison between Shuttles, Buses, Ridesharing and Taxis
- Authors
- Samuel Chng (Corresponding Author) - Singapore University of Technology and DesignSabreena Anowar - University of MissouriLynette Cheah - Singapore University of Technology and Design
- Publication details
- Sustainability , Vol.14(20), pp.1-14
- Publisher
- MDPI AG
- Date published
- 2022
- DOI
- 10.3390/su142013656
- ISSN
- 2071-1050
- Copyright note
- © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
- Data Availability
- Restrictions apply to the availability of the data presented in this study. The data are not publicly available due to funding requirements.
- Organisation Unit
- School of Science, Technology and Engineering
- Language
- English
- Record Identifier
- 991029278902621
- Output Type
- Journal article
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