TY - JOUR
T1 - A literature review of Artificial Intelligence applications in railway systems
AU - Tang, Ruifan
AU - De Donato, Lorenzo
AU - Bes̆inović, Nikola
AU - Flammini, Francesco
AU - Goverde, Rob M.P.
AU - Lin, Zhiyuan
AU - Liu, Ronghui
AU - Tang, Tianli
AU - Vittorini, Valeria
AU - Wang, Ziyulong
PY - 2022
Y1 - 2022
N2 - Nowadays it is widely accepted that Artificial Intelligence (AI) is significantly influencing a large number of domains, including railways. In this paper, we present a systematic literature review of the current state-of-the-art of AI in railway transport. In particular, we analysed and discussed papers from a holistic railway perspective, covering sub-domains such as maintenance and inspection, planning and management, safety and security, autonomous driving and control, revenue management, transport policy, and passenger mobility. This review makes an initial step towards shaping the role of AI in future railways and provides a summary of the current focuses of AI research connected to rail transport. We reviewed about 139 scientific papers covering the period from 2010 to December 2020. We found that the major research efforts have been put in AI for rail maintenance and inspection, while very limited or no research has been found on AI for rail transport policy and revenue management. The remaining sub-domains received mild to moderate attention. AI applications are promising and tend to act as a game-changer in tackling multiple railway challenges. However, at the moment, AI research in railways is still mostly at its early stages. Future research can be expected towards developing advanced combined AI applications (e.g. with optimization), using AI in decision making, dealing with uncertainty and tackling newly rising cybersecurity challenges.
AB - Nowadays it is widely accepted that Artificial Intelligence (AI) is significantly influencing a large number of domains, including railways. In this paper, we present a systematic literature review of the current state-of-the-art of AI in railway transport. In particular, we analysed and discussed papers from a holistic railway perspective, covering sub-domains such as maintenance and inspection, planning and management, safety and security, autonomous driving and control, revenue management, transport policy, and passenger mobility. This review makes an initial step towards shaping the role of AI in future railways and provides a summary of the current focuses of AI research connected to rail transport. We reviewed about 139 scientific papers covering the period from 2010 to December 2020. We found that the major research efforts have been put in AI for rail maintenance and inspection, while very limited or no research has been found on AI for rail transport policy and revenue management. The remaining sub-domains received mild to moderate attention. AI applications are promising and tend to act as a game-changer in tackling multiple railway challenges. However, at the moment, AI research in railways is still mostly at its early stages. Future research can be expected towards developing advanced combined AI applications (e.g. with optimization), using AI in decision making, dealing with uncertainty and tackling newly rising cybersecurity challenges.
KW - Artificial Intelligence
KW - Autonomous driving
KW - Machine Learning
KW - Maintenance
KW - Railways
KW - Smart mobility
KW - Traffic management
KW - Train control
KW - Transportation
UR - http://www.scopus.com/inward/record.url?scp=85129622349&partnerID=8YFLogxK
U2 - 10.1016/j.trc.2022.103679
DO - 10.1016/j.trc.2022.103679
M3 - Review article
AN - SCOPUS:85129622349
SN - 0968-090X
VL - 140
SP - 1
EP - 25
JO - Transportation Research Part C: Emerging Technologies
JF - Transportation Research Part C: Emerging Technologies
M1 - 103679
ER -