Project objectives

The overall objective of the RAILS research project is to investigate the potential of Artificial Intelligence (AI) in the rail sector and contribute to the definition of roadmaps for future research in next generation signalling systems, operational intelligence, and network management. RAILS will address the training of PhD students to support the research capacity in AI within the rail sector across Europe by involving research institutions in four different countries (Italy, UK, Netherlands, and Sweden), with a combined background in both computer science and transportation systems.

The RAILS project aims at developing roadmaps for fast uptake of Artificial Intelligence in the railway sector by identifying effective and suitable techniques and testing methods for AI and assessing impacts towards improving overall performance of the railway system as a whole.

To meet the needs of S2R, RAILS will produce knowledge, ground breaking research and experimental proof-of-concept for the adoption of AI in rail automation, predictive maintenance and defect detection, traffic planning and capacity optimization. As such, RAILS wants to effectively contribute to the design and implementation of smarter railways.

To reach this goal, RAILS will address the following key objectives:

Objective 1. Identification of the potential of AI for railways: develop a comprehensive and up-to-date overview of relevant state-of-the-art of AI approaches, innovation technologies and trends applicable to railways from the transport sector and other relevant sectors.

Objective 2. Adherence to current work in railways innovation: line-up the research activities with available results from relevant ongoing projects and initiatives in the railway sector including Shift2Rail projects and relevant European Technology Platforms (e.g., RRAC – The European Technology Platform on Rail Research, U-EIP – European ITS Platform).

Objective 3. Recognition of required innovation shifts: determine the gaps between AI potential, possible future scenarios and applications with the status-quo in the rail sector.

Objective 4. Development of methodological and experimental proof-of-concepts: pilot studies providing feasibility studies for the adoption of A.I and related techniques (e.g., Big Data Analytics) in: 1) safety and rail automation, 2) predictive maintenance and defect detection, 3) traffic planning and management.

Objective 5. Development of Benchmarks, Models and Simulations: validation of the technical soundness, deployment feasibility, and industrial applicability of the methodological and technological concepts developed in RAILS.

Objective 6. Transition pathways toward the rail system scenario: identification of the new research directions to improve reliability, maintainability, safety, cyber-physical security, and performance through the adoption of AI.

Objective 7. Involvement of relevant rail stakeholders: collaboration with industries and railway operators to meet a multi-facet objective:

  • to gain data, use cases, feedback and valuable inputs and insights;
  • to disseminate and exploit project results;
  • to promote the development of AI education, innovation and best practices in railway industrial and operational settings.

Objective 8. Training of young researchers and creation of a research network on AI in railways: RAILS will provide a comprehensive formative experience to PhD students undertaking challenging research through:

  • mobility across the academic institutions of the consortium and the guidance of leading senior scientists;
  • multi-disciplinary collaboration among researchers belonging to different scientific sectors across Europe.