Reference
W. Phusakulkajorn, A. Núñez, H. Wang, A. Jamshidi, A. Zoeteman,
B. Ripke, R. Dollevoet, B. De Schutter, and Z. Li, "Artificial intelligence in
railway infrastructure: Current research, challenges, and future
opportunities,"
Intelligent Transportation
Infrastructure, vol. 2, 2023. Paper liad016.
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.
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BibTeX
@article{PhuNun:23-028,
author = {Phusakulkajorn, Wassamon and N{\'{u}}{\~{n}}ez, Alfredo and Wang,
Hongrui and Jamshidi, Ali and Zoeteman, Arjen and Ripke, Burchard
and Dollevoet, Rolf and De Schutter, Bart and Li, Zili},
title = {Artificial Intelligence in Railway Infrastructure: Current
Research, Challenges, and Future Opportunities},
journal = {Intelligent Transportation Infrastructure},
volume = {2},
year = {2023},
note = {Paper liad016}
}