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Recommendations and Roadmaps Towards Intelligent Railways

Book Chapter (2025)
Author(s)

Lorenzo De Donato (Università degli Studi di Napoli Federico II)

Ruifan Tang (University of Leeds)

Nikola Bešinović (Technische Universität Dresden, Transport and Planning)

Francesco Flammini (Mälardalen University, University of Applied Sciences and Arts of Southern Switzerland, Linnaeus University - Växjö)

Rob M.  P. Goverde (TU Delft - Civil Engineering & Geosciences)

Zhiyuan Lin (University of Leeds)

Ronghui Liu (University of Leeds)

Stefano Marrone (Università degli Studi di Napoli Federico II)

Elena Napoletano (Università degli Studi di Napoli Federico II)

Roberto Nardone (Università degli Studi di Napoli Parthenope)

Stefania Santini (Università degli Studi di Napoli Federico II)

Valeria Vittorini (Università degli Studi di Napoli Federico II)

Research Group
Transport, Mobility and Logistics
DOI related publication
https://doi.org/10.1007/978-3-032-06763-0_25 Final published version
More Info
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Publication Year
2025
Language
English
Research Group
Transport, Mobility and Logistics
Pages (from-to)
169-175
Publisher
Springer
ISBN (print)
978-3-032-06762-3
ISBN (electronic)
978-3-032-06763-0
Downloads counter
105
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Abstract

This paper provides an overview of the main results achieved within the Horizon 2020 Shift2Rail project named RAILS (Roadmaps for Artificial Intelligence Integration in the Rail Sector). The RAILS roadmapping process provided state-of-the-art, taxonomy, future research directions, and recommendations in three macro areas: Railway Safety and Automation, Predictive Maintenance and Defect Detection, and Traffic Planning and Management. RAILS findings shed light on the potential of intelligent technologies and provided essential guidelines for integrating machine learning into next-generation smart railways.