Recommendations and Roadmaps Towards Intelligent Railways

Publikation: Beitrag in Buch/Konferenzbericht/Sammelband/GutachtenBeitrag in KonferenzbandBeigetragenBegutachtung

Beitragende

  • Lorenzo De Donato - , Università degli Studi di Napoli Federico II (Autor:in)
  • Ruifan Tang - , University of Leeds (Autor:in)
  • Nikola Bešinović - , Professur für Betrieb von Bahnsystemen, Technische Universität Delft (Autor:in)
  • Francesco Flammini - , Scuola universitaria professionale della Svizzera italiana, Mälardalen University, Linnaeus University (Autor:in)
  • Rob M  P Goverde - , Technische Universität Delft (Autor:in)
  • Zhiyuan Lin - , University of Leeds (Autor:in)
  • Ronghui Liu - , University of Leeds (Autor:in)
  • Stefano Marrone - , Università degli Studi di Napoli Federico II (Autor:in)
  • Elena Napoletano - , Università degli Studi di Napoli Federico II (Autor:in)
  • Roberto Nardone - , University of Naples Parthenope (Autor:in)
  • Stefania Santini - , Università degli Studi di Napoli Federico II (Autor:in)
  • Valeria Vittorini - , Università degli Studi di Napoli Federico II (Autor:in)

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

OriginalspracheEnglisch
TitelTransport Transitions: Advancing Sustainable and Inclusive Mobility
Herausgeber (Verlag)Springer
Seiten169-175
Seitenumfang7
ISBN (elektronisch)978-3-032-06763-0
ISBN (Print)978-3-032-06762-3
PublikationsstatusVeröffentlicht - 2026
Peer-Review-StatusJa

Publikationsreihe

ReiheLecture Notes in Mobility
BandPart F1025
ISSN2196-5544

Externe IDs

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

Schlagworte

Schlagwörter

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