A Systematic Review of Artificial Intelligence Public Datasets for Railway Applications

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MARC

Pappaterra, Mauro JoseUppsala universitet,Linnéuniversitetet,Institutionen för datavetenskap och medieteknik (DM),Department of Computer Science, Uppsala University, Uppsala, Sweden,Institutionen för informationsteknologi,Linnaeus Univ, Dept Comp Sci & Media Technol, S-35195 Växjö, Sweden. (author)

A Systematic Review of Artificial Intelligence Public Datasets for Railway Applications

Article/chapterEnglish2021

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2021-09-22
MDPI,2021
printrdacarrier

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LIBRIS-ID:oai:DiVA.org:lnu-108233
https://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-108233URI
https://doi.org/10.3390/infrastructures6100136DOI
https://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-56127URI
https://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-458800URI

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Language:English
Summary in:English

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Subject category:for swepub-publicationtype

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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.

Subject headings and genre

NATURVETENSKAP Data- och informationsvetenskap Datavetenskap hsv//swe
NATURAL SCIENCES Computer and Information Sciences Computer Sciences hsv//eng
TEKNIK OCH TEKNOLOGIER Samhällsbyggnadsteknik Transportteknik och logistik hsv//swe
ENGINEERING AND TECHNOLOGY Civil Engineering Transport Systems and Logistics hsv//eng
NATURVETENSKAP Data- och informationsvetenskap hsv//swe
NATURAL SCIENCES Computer and Information Sciences hsv//eng
railways
public datasets
intelligent transportation
machine learning
predictive maintenance
Computer Science
Datavetenskap

Added entries (persons, corporate bodies, meetings, titles ...)

Flammini, Francesco,Senior Lecturer,1978-Mälardalens högskola,Linnéuniversitetet,Institutionen för datavetenskap och medieteknik (DM),Mälardalen University, Sweden,ERES;DISA-SIG,Innovation och produktrealisering,Linnaeus University, Växjö, Sweden,Linnaeus Univ, Dept Comp Sci & Media Technol, S-35195 Växjö, Sweden.;Mälardalen Univ, Sch Innovat Design & Engn, S-63220 Eskilstuna, Sweden.(Swepub:mdh)ffi01 (author)
Vittorini, ValeriaUniv Naples Federico II, Dept Elect Engn & Informat Technol, I-80138 Naples, Italy.,University of Naples Federico II, Naples, Italy (author)
Besinovic, NikolaDelft Univ Technol, Dept Transport & Planning, NL-2628 CN Delft, Netherlands.,Delft University of Technology, Delft, Netherlands (author)
LinnéuniversitetetInstitutionen för datavetenskap och medieteknik (DM) (creator_code:org_t)

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