Enhancing the ERTMS with Moving Block System, Automatic Train Operations and GNSS positioning can ensure the competitiveness of the European railway industry, and, in the meanwhile, guarantee a concrete improvement in the quality of the European railway transport system, solving the problem of increasing demand on high density lines.
On the other hand, the innovation process of the railway transport is necessarily slower compared with the pace at which new technological advances become available today: other transport sectors are evolving more rapidly and this difference is well motivated by the robust safety strictly required by the railways.
For this reason, it seems promising the idea of borrowing solutions form other transportation domains, but only by adapting them to the railway context after their careful evaluation and validation from the very beginning, also leveraging formal methods.

The keywords highlighted before well represent the ASTRail rationale and aims which are split into 4 main technical work streams (WSes):
•  WS1: GNSS technology into the ERTMS Signalling System
•  WS2: Hazard Analysis of the railway system (with a focus on “moving block signalling”)
•  WS3: Automatic driving technologies for Automatic Train Operations
•  WS4: Formal language and method to be applied in the railway field
The WS-es are only apparently separate and, on the contrary, they have strong interactions. In fact, the understanding of GNSS performance in railways will be crucial for its adoption, particularly in the new moving block signalling and for its integration within the solutions for the Automatic Train Operation; formal methods will be crucial to perform the hazard analysis of new signalling methods.

ASTRail is a RIA funded under the call H2020-S2RJU-2017 and, in its aims it perfectly reflects the position of the Shift2Rail (S2R) Joint Undertaking, as stated in their Multi-Annual Action Plan (MAAP) roadmap -in particular in the Innovation Programme 2.

 

This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No: 777561

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