Yin, M, Li, K orcid.org/0000-0001-6657-0522 and Xiaoqing, C (2020) A review on artificial intelligence in high-speed rail. Transportation Safety and Environment, 2 (4). tdaa022. pp. 247-259. ISSN 2631-4428
Abstract
High-speed rail (HSR) has brought a number of social and economic benefits, such as shorter trip times for journeys of between one and five hours; safety, security, comfort and on-time commuting for passengers; energy saving and environmental protection; job creation; and encouraging sustainable use of renewable energy and land. The recent development in HSR has seen the pervasive applications of artificial intelligence (AI). This paper first briefly reviews the related disciplines in HSR where AI may play an important role, such as civil engineering, mechanical engineering, electrical engineering and signalling and control. Then, an overview of current AI techniques is presented in the context of smart planning, intelligent control and intelligent maintenance of HSR systems. Finally, a framework of future HSR systems where AI is expected to play a key role is provided.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © The Author(s) 2020. Published by Oxford University Press on behalf of Central South University Press. This is an open access article under the terms of the Creative Commons Attribution 4.0 International (CC BY 4.0) (https://creativecommons.org/licenses/by/4.0/) |
Keywords: | high-speed rail; artificial intelligence; smart planning; intelligent control; intelligent maintenance |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Electronic & Electrical Engineering (Leeds) > Institute of Communication & Power Networks (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 18 Aug 2020 10:38 |
Last Modified: | 21 Apr 2021 15:17 |
Status: | Published |
Publisher: | Oxford University Press |
Identification Number: | 10.1093/tse/tdaa022 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:164470 |