Artificial Intelligence in Railway Infrastructure

Current Research, Challenges, and Future Opportunities

Literature review (2023)

Authors

W. Phusakulkajorn Railway Engineering - CEG
Alfredo Nunez Railway Engineering - CEG
H. Wang Railway Engineering - CEG
Ali Jamshidi Praedico
A. Zoeteman Railway Engineering - CEG
Burchard Ripke Deutsche Bahn
R.P.B.J. Dollevoet Railway Engineering - CEG
B.H.K. De Schutter Delft Center for Systems and Control
Z. Li Railway Engineering - CEG
Research Group
Railway Engineering (CEG) (TU Delft)
Machine Learning Artificial Intelligence Railway Infrastructure Railway Track Railway Catenary
More Info
expand_more

Published Date

2023

Language

English

Reuse Rights

Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.

Faculty

Civil Engineering & Geosciences

Department

Engineering Structures

Research Group

Railway Engineering

Abstract

The railway industry has the potential to strongly contribute to achieving various sustainable development goals by expanding its role in the transportation system of different countries. To realize that, complex technological and societal challenges are to be addressed, along with the development of suitable state-of-the-art methodologies fully tailored to the particular needs of the wide variety of railway infrastructure types and conditions. Artificial intelligence (AI) methods have been increasingly and successfully applied to solve practical problems in the railway infrastructure domain for over two decades. This paper proposes a review of the development of AI methods in railway infrastructure. First, we present a survey limited to selected journal papers published between 2010-2022. Bibliographical statistics are obtained, showing the increasing number of contributions in this field. Then, we select key AI methodologies and discuss their applications in the railway infrastructure. Next, AI methods for key railway components are analyzed. Finally, current challenges and future opportunities are discussed.