Recommendations and Roadmaps Towards Intelligent Railways

Research output: Contribution to book/Conference proceedings/Anthology/ReportConference contributionContributedpeer-review

Contributors

  • Lorenzo De Donato - , Universita' di Napoli Federico II (Author)
  • Ruifan Tang - , University of Leeds (Author)
  • Nikola Bešinović - , Chair of Railway Operations, Delft University of Technology (Author)
  • Francesco Flammini - , University of Applied Sciences and Arts of Southern Switzerland, Mälardalen University, Linnaeus University (Author)
  • Rob M  P Goverde - , Delft University of Technology (Author)
  • Zhiyuan Lin - , University of Leeds (Author)
  • Ronghui Liu - , University of Leeds (Author)
  • Stefano Marrone - , Universita' di Napoli Federico II (Author)
  • Elena Napoletano - , Universita' di Napoli Federico II (Author)
  • Roberto Nardone - , University of Naples Parthenope (Author)
  • Stefania Santini - , Universita' di Napoli Federico II (Author)
  • Valeria Vittorini - , Universita' di Napoli Federico II (Author)

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.

Details

Original languageEnglish
Title of host publicationTransport Transitions: Advancing Sustainable and Inclusive Mobility
PublisherSpringer
Pages169-175
Number of pages7
ISBN (electronic)978-3-032-06763-0
ISBN (print)978-3-032-06762-3
Publication statusPublished - 2026
Peer-reviewedYes

Publication series

SeriesLecture Notes in Mobility
VolumePart F1025
ISSN2196-5544

External IDs

ORCID /0000-0003-4111-2255/work/196694772

Keywords

Keywords

  • Artificial Intelligence, Autonomous Trains, Machine Learning, Smart Maintenance, Train Delay Prediction