Wadud, Z orcid.org/0000-0003-2692-8299
(2017)
Fully automated vehicles: A cost of ownership analysis to inform early adoption.
Transportation Research Part A: Policy and Practice, 101.
pp. 163-176.
ISSN 0965-8564
Abstract
Vehicle automation and its uptake is an active area of research among transportation academics. Early adoption rate also influences the results in other areas, e.g. the potential impacts of vehicle automation. So far, most of the interest in the uptake of fully automated, driverless vehicles has focused on private vehicles only, yet full automation could be beneficial for commercial vehicles too. This paper identifies the vehicle sectors that will likely be the earliest adopters of full automation. Total costs of ownership (TCO) analysis is used to compare the costs (and benefits) of vehicle automation for private vehicles among different income groups and commercial vehicles in the taxi and freight sectors in the UK. Commercial operations clearly benefit more from automation since the driver costs can be reduced substantially through automation. Among the private users, households with the highest income benefit more from automation because of their higher driving distances and higher perceived value of time, which can be used more productively through full automation.
Metadata
Item Type: | Article |
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Authors/Creators: | |
Copyright, Publisher and Additional Information: | © 2017 Elsevier Ltd. This is an author produced version of a paper published in Transportation Research Part A: Policy and Practice. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | vehicle automation; driverless car; autonomous vehicles; total cost of ownership; travel time use; early adoption |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Chemical & Process Engineering (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 04 May 2017 10:00 |
Last Modified: | 17 May 2018 00:38 |
Status: | Published |
Publisher: | Elsevier |
Identification Number: | 10.1016/j.tra.2017.05.005 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:115936 |