by Francesco Flammini (Mälardalen University), Stefano Marrone (University of Campania Luigi Vanvitelli) and Lei Chen (University of Birmingham)

PERFORMINGRAIL aims to delineate, through formal modelling and optimal traffic management, moving block railway signalling using advanced train positioning approaches for diverse market segments.

In order to address several challenges coming from the need to optimise their operational capacity, especially in highly congested corridors, future railways need to implement the new paradigms enabled by recent technological development, such as moving block signalling [1], satellite-based train positioning, and virtual coupling [2]. Those technologies have the potential to significantly increase the performance/cost ratio; however, they also pose several safety concerns that need to be formalised and carefully addressed by novel modelling and simulation approaches [3]. 

To tackle those challenges, the PERFORMINGRAIL project has been granted by the European Union Horizon 2020 framework program within Shift2Rail (S2R) Joint Undertaking (JU), Innovation Programme 2 (IP2) “Advanced traffic management and control systems”, with a specific call addressing “Modelling of the Moving Block system specification and future architecture (TD2.3) + RAIM algorithms, Assessment Report and support for Railway Minimum Operational Performance Standards (TD2.4)” (call code: S2R-OC-IP2-01-2020).
The aim of the project is to implement a holistic system approach to address the open challenges for the Moving Block and Virtual Coupling concepts in terms of safe operational principles and specifications, high-accuracy train localisation and optimised moving block traffic management algorithms. The main objectives of the project are to enhance and verify existing specifications for moving block signalling, while developing formal models, algorithms, and proof of concepts to test and validate an integrated future moving block system architecture that will provide safe and efficient operational performances.

PERFORMINGRAIL will support the activities in the Shift2Rail IP2 Adaptable Communications Technology Demonstrator by helping to bring the developments as close as possible to the market while also helping to update the regulatory framework.

In accordance with the objectives of the IP2 TD2.4 Fail Safe Train Positioning (including GNSS) described in the S2R Multi Annual Action Plan (MAAP), in the field of Train Localisation (based on the use of combined technologies such as EGNSS, IMU, kinematics, Digital Map), PERFORMINGRAIL will:

  • contribute Enhanced railways Fault Detection and Exclusion algorithms for addressing local feared events, and Data Fusion algorithms suitable for railway safe applications;
  • contribute EGNSS monitoring techniques based on carrier phase measurement and multi-frequency technology
  • help promote the use of formal methods to check safety of advanced railway operation (in accordance with IP2 TD2.7);
  • provide an Independent Assessment Report on the proposed technologies and solutions.

The project is structured in seven Work Packages, including five technical Work Packages (WP1 – WP5), plus WP6 about dissemination and exploitation, and WP7 for project administration and management.

Key technical objectives of the PERFORMINGRAIL project:

  • OBJECTIVE 1: Definition of Specifications for safety and performance of moving block operations;
  • OBJECTIVE 2: Formal modelling of moving block specifications;
  • OBJECTIVE 3: Identification of moving block hazards;
  • OBJECTIVE 4: Development of fail-safe train localisation solution;
  • OBJECTIVE 5: Design of future traffic management architecture for moving block;
  • OBJECTIVE 6: Implementation of a moving block testing platform;
  • OBJECTIVE 7: Proof-of-concept and assessment of moving block specifications and models;
  • OBJECTIVE 8: Recommendations for safety and performance of moving block configuratio;

In order to achieve those objectives, the research activities within PERFORMINGRAIL with be based on the results in cooperation with other S2R complementary projects such as X2RAIL-5 and X2RAIL-3.

Project goals can be achieved by adopting a model-driven and compositional modelling approach, which also naturally fits the EULYNX modelling method, as the formal models can be automatically derived from SysML and other high-level engineering specification languages through proper model transformations. The construction of the system models will be compositional, according to a bottom-up development methodology, enabling model parameterisation and consequently the possibility to easily adapt the models to different system configurations. So, model-driven techniques support automation, whereas compositionality allows for a major flexibility in modelling, promotes the reuse of models and facilitates the definition of modelling guidelines.

The reference architecture can be instantiated to define a proper tool chain also using standard languages and existing tools already employed by industries. It is characterised by three tiers, three actors and three layers (see Figure 1). The three actors represent different classes of users that may develop: domain-specific modeling languages (Language Engineer), transformations (Software Engineer) or models (System Modeler). The three horizontal layers (Artefact, Tool and Repository) represent the entities (models, tools and databases) involved in each tier. The three vertical tiers contain:

  • Artefacts, tools and storage elements associated with non-automatic tasks (User);
  • High-Level Models (e.g., UML models), intermediate formal models, Model-to-Model (M2M) and Model-to-Text (M2T) transformations and repositories associated with automatic tasks (Transformation);
  • Final (concrete) models and tools for the analysis and the solution (Analysis).

In addition, a Model Composer is considered to compose sub-models into a single model if needed. The Feedback Engine is in charge of updating the High-Level Models after the analysis or test case generation phases.

PERFORMINGRAIL is coordinated by the University of Birmingham (UK), through its Birmingham Centre for Railway Research and Education (BCRRE), and project partners include TU Delft (NL), University Gustave Eiffel (FR), CINI (IT), Mälardalen University (SE), CERTIFER (FR), Rokubun (ES), and Eulynx (NL).

At the time of writing, the project has already achieved its first milestone (MS1) on schedule with the submission of deliverables:

  • D1.1 Baseline system specification and definition for Moving Block Systems;
  • D2.1: Modelling guidelines and Moving Block Use Cases characterisation.

Additional and up-to-date information about the project is available on the project website [L1] and social channels [L2], while technical project deliverables and research papers are also shared on ResearchGate [L3].

This project has received funding from the Shift2Rail Joint Undertaking (JU) under grant agreement No 101015416. The JU receives support from the European Union’s Horizon 2020 research and innovation programme and the Shift2Rail JU members other than the Union.

Figure 1: The DAIS consortium.
Figure 1: The DAIS consortium.

Links:
[L1] https://www.performingrail.com/ 
[L2] https://twitter.com/PerformingRail
[L3] https://www.researchgate.net/project/PERFORMINGRAIL-PERformance-based-Formal-modelling-and-Optimal-tRaffic-Management-for-movING-block-RAILway-signalling

References:
[1] L. Carnevali, et al.: “Non-Markovian Performability Evaluation of ERTMS/ETCS Level 3”, in Beltrán M., Knottenbelt W., Bradley J. (eds): “Computer Performance Engineering EPEW 2015”, Lecture Notes in Computer Science, vol 9272, Springer, Cham.(2015) https://doi.org/10.1007/978-3-319-23267-6_4
[2] F. Flammini, et al.: “Compositional modeling of railway Virtual Coupling with Stochastic Activity Networks”, Formal Aspects of Computing (2021) https://doi.org/10.1007/s00165-021-00560-5
[3] D. Basile, et al.: “Modelling and Analysing ERTMS L3 Moving Block Railway Signalling with Simulink and Uppaal SMC”, in Larsen K., Willemse T. (eds): “Formal Methods for Industrial Critical Systems”, Lecture Notes in Computer Science, vol 11687, Springer, Cham. (2019) https://doi.org/10.1007/978-3-030-27008-7_1

Please contact:
Lei Chen (Project Coordinator), The University of Birmingham, United Kingdom, This email address is being protected from spambots. You need JavaScript enabled to view it.
Francesco Flammini (Dissemination Manager), Mälardalen University, Sweden, This email address is being protected from spambots. You need JavaScript enabled to view it.

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