RT
R. Tang
info
Please Note
<p>This page displays the records of the person named above and is not linked to a unique person identifier. This record may need to be merged to a profile.</p>
5 records found
1
Book chapter
(2025)
-
Lorenzo De Donato, Ruifan Tang, Stefania Santini, Valeria Vittorini, Nikola Bešinović, Francesco Flammini, Rob M. P. Goverde, Zhiyuan Lin, Ronghui Liu, Stefano Marrone, Elena Napoletano, Roberto Nardone
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.
...
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.
Artificial intelligence in railways
Current applications, challenges, and ongoing research
Book chapter
(2023)
-
Lorenzo De Donato, Ruifan Tang, Nikola Besĭnović, Francesco Flammini, Rob M.P. Goverde, Zhiyuan Lin, Ronghui Liu, Stefano Marrone, Elena Napoletano, More authors...
This chapter presents applications, challenges, and opportunities for the integration of artificial intelligence in rail transport, based on the current results of the European project Roadmaps for AI integration in the rail sector (RAILS). Past and ongoing research directions are briefly outlined, and then the regulatory landscape is presented as well as the main barriers to overcome. Some technical aspects are addressed to provide some valuable references, and a high-level description of ongoing research work is given, spanning from innovative studies on smart maintenance, collision avoidance, delay prediction, and incident attribution analysis to visionary scenarios such as intelligent control and virtual coupling.
...
This chapter presents applications, challenges, and opportunities for the integration of artificial intelligence in rail transport, based on the current results of the European project Roadmaps for AI integration in the rail sector (RAILS). Past and ongoing research directions are briefly outlined, and then the regulatory landscape is presented as well as the main barriers to overcome. Some technical aspects are addressed to provide some valuable references, and a high-level description of ongoing research work is given, spanning from innovative studies on smart maintenance, collision avoidance, delay prediction, and incident attribution analysis to visionary scenarios such as intelligent control and virtual coupling.
In this chapter, applications of artificial intelligence (AI) in railway traffic planning and management (RTPM) are discussed. To begin, a definition of AI is offered with a particular emphasis on its relationship with RTPM. This is followed by a systematic literature review of the state-of-the-art of AI in RTPM covering strategic, tactical, and operational challenges. Next, a transferability analysis is conducted of AI approaches for traffic planning and management from related sectors to railways, specifically from aviation and road transport. The results show that the majority of AI research in RTPM is still in its infancy. Several future research areas that are important to academic and professional communities in AI and RTPM are identified based on reviews and analysis of transferability.
...
In this chapter, applications of artificial intelligence (AI) in railway traffic planning and management (RTPM) are discussed. To begin, a definition of AI is offered with a particular emphasis on its relationship with RTPM. This is followed by a systematic literature review of the state-of-the-art of AI in RTPM covering strategic, tactical, and operational challenges. Next, a transferability analysis is conducted of AI approaches for traffic planning and management from related sectors to railways, specifically from aviation and road transport. The results show that the majority of AI research in RTPM is still in its infancy. Several future research areas that are important to academic and professional communities in AI and RTPM are identified based on reviews and analysis of transferability.