Bešinović, N, De Donato, L, Flammini, F et al. (7 more authors) (2022) Artificial Intelligence in Railway Transport: Taxonomy, Regulations, and Applications. IEEE Transactions on Intelligent Transportation Systems, 23 (9). pp. 14011-14024. ISSN 1524-9050
Abstract
Artificial Intelligence (AI) is becoming pervasive in most engineering domains, and railway transport is no exception. However, due to the plethora of different new terms and meanings associated with them, there is a risk that railway practitioners, as several other categories, will get lost in those ambiguities and fuzzy boundaries, and hence fail to catch the real opportunities and potential of machine learning, artificial vision, and big data analytics, just to name a few of the most promising approaches connected to AI. The scope of this paper is to introduce the basic concepts and possible applications of AI to railway academics and practitioners. To that aim, this paper presents a structured taxonomy to guide researchers and practitioners to understand AI techniques, research fields, disciplines, and applications, both in general terms and in close connection with railway applications such as autonomous driving, maintenance, and traffic management. The important aspects of ethics and explainability of AI in railways are also introduced. The connection between AI concepts and railway subdomains has been supported by relevant research addressing existing and planned applications in order to provide some pointers to promising directions.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |
Keywords: | Artificial intelligence , railway transport , machine learning , computer vision , traffic management , predictive maintenance |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Environment (Leeds) > Institute for Transport Studies (Leeds) > ITS: Spatial Modelling and Dynamics (Leeds) |
Funding Information: | Funder Grant number EU - European Union 881782 Rail Safety & Standards Board Not Known |
Depositing User: | Symplectic Publications |
Date Deposited: | 29 Nov 2021 15:10 |
Last Modified: | 15 Nov 2023 12:24 |
Status: | Published |
Publisher: | IEEE |
Identification Number: | 10.1109/TITS.2021.3131637 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:180822 |