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A Systematic Review of Artificial Intelligence Public Datasets for Railway Applications
Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM). Uppsala University, Sweden.
Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM). Mälardalen University, Sweden. (ERES;DISA-SIG)ORCID iD: 0000-0002-2833-7196
Univ Naples Federico II, Italy.
Delft Univ Technol, Netherlands.
2021 (English)In: Infrastructures, E-ISSN 2412-3811, Vol. 6, no 10, article id 136Article, review/survey (Refereed) Published
Abstract [en]

The aim of this paper is to review existing publicly available and open artificial intelligence (AI) oriented datasets in different domains and subdomains of the railway sector. The contribution of this paper is an overview of AI-oriented railway data published under Creative Commons (CC) or any other copyright type that entails public availability and freedom of use. These data are of great value for open research and publications related to the application of AI in the railway sector. This paper includes insights on the public railway data: we distinguish different subdomains, including maintenance and inspection, traffic planning and management, safety and security and type of data including numerical, string, image and other. The datasets reviewed cover the last three decades, from January 1990 to January 2021. The study revealed that the number of open datasets is very small in comparison with the available literature related to AI applications in the railway industry. Another shortcoming is the lack of documentation and metadata on public datasets, including information related to missing data, collection schemes and other limitations. This study also presents quantitative data, such as the number of available open datasets divided by railway application, type of data and year of publication. This review also reveals that there are openly available APIs-maintained by government organizations and train operating companies (TOCs)-that can be of great use for data harvesting and can facilitate the creation of large public datasets. These data are usually well-curated real-time data that can greatly contribute to the accuracy of AI models. Furthermore, we conclude that the extension of AI applications in the railway sector merits a centralized hub for publicly available datasets and open APIs.</p>

Place, publisher, year, edition, pages
MDPI, 2021. Vol. 6, no 10, article id 136
Keywords [en]
railways, public datasets, intelligent transportation, machine learning, predictive maintenance
National Category
Computer Sciences Transport Systems and Logistics
Research subject
Computer and Information Sciences Computer Science, Computer Science
Identifiers
URN: urn:nbn:se:lnu:diva-108233DOI: 10.3390/infrastructures6100136ISI: 000712673400001Scopus ID: 2-s2.0-85115856872Local ID: 2021OAI: oai:DiVA.org:lnu-108233DiVA, id: diva2:1614746
Available from: 2021-11-26 Created: 2021-11-26 Last updated: 2024-08-28Bibliographically approved

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Flammini, Francesco

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