A literature review of Artificial Intelligence applications in railway systems

被引:110
作者
Tang, Ruifan [1 ]
De Donato, Lorenzo [2 ]
Besinovic, Nikola [3 ]
Flammini, Francesco [4 ,5 ]
Goverde, Rob M. P. [3 ]
Lin, Zhiyuan [1 ,7 ]
Liu, Ronghui [1 ]
Tang, Tianli [1 ,6 ]
Vittorini, Valeria [2 ]
Wang, Ziyulong [3 ]
机构
[1] Univ Leeds, Inst Transport Studies, 34-40 Univ Rd, Leeds LS2 9JT, England
[2] Univ Naples Federico II, Dept Elect Engn & Informat Technol, Via Claudio 21, I-80125 Naples, Italy
[3] Delft Univ Technol, Dept Transport & Planning, POB 5048, NL-2600 GA Delft, Netherlands
[4] Linnaeus Univ, Dept Comp Sci & Media Technol, Vaxjo, Sweden
[5] Malardalen Univ, Sch Innovat Design & Engn, Vasteras, Sweden
[6] Southeast Univ, Jiangsu Prov Collaborat Innovat Ctr Modern Urban T, Sch Transportat, Jiangsu Key Lab Urban ITS, Nanjing 211189, Peoples R China
[7] Univ Leeds, 34-40 Univ Rd, Leeds LS2 9JT, England
关键词
Artificial Intelligence; Railways; Transportation; Machine Learning; Autonomous driving; Maintenance; Smart mobility; Train control; Traffic management; AUTOMATED VISUAL INSPECTION; CONDITION-BASED MAINTENANCE; BIG DATA; TRANSPORTATION SYSTEMS; TRACK MAINTENANCE; LEARNING APPROACH; DEFECT DETECTION; PREDICTION; OPTIMIZATION; MODEL;
D O I
10.1016/j.trc.2022.103679
中图分类号
U [交通运输];
学科分类号
08 ; 0823 ;
摘要
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.
引用
收藏
页数:25
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