Predictive maintenance is gaining traction as a concept in rail infrastructure management. The investment costs involved are substantial, however, and infrastructure managers have lacked a standardised method for evaluating whether and when specific predictive maintenance solutions pay off.

A project commissioned by DZSF and led by TÜV Rheinland InterTraffic GmbH, with Porsche Consulting, pom+ Deutschland, DB InfraGO, and Verkehrsgesellschaft Frankfurt am Main as subcontractors, addressed this directly. Its objective was to make the economic potential of digital and data-based predictive maintenance technologies assessable, by developing a unified framework for economic and amortisation analysis of complete predictive maintenance solutions across the infrastructure lifecycle.

The project covered the full range of maintenance planning levels, analysed life cycle costs, and examined how individual planning tasks at different operational levels can benefit from predictive maintenance methods. A standardised assessment method for predictive maintenance investment decisions was the central intended output, designed to accelerate adoption by making the business case for specific solutions comparable and transparent.

The project ran for 30 months from August 2023.

NEXUS aims to transform metro systems by developing flexible, demand-responsive capacity, optimising train operations, and enhancing sustainability. Deliverable D7.1 documents the validation of metro system simulation models and newly developed AI applications for mass rapid transit. The work evaluated the strengths and weaknesses of current metro systems and assessed the potential impacts of fully automated GoA4 operations across a range of passenger demand scenarios and disruption cases, including energy and CO₂ optimisation and cybersecurity considerations. The validated models will be used to simulate proposed future improvement measures.

FP2-R2DATO advances autonomous rail freight operations across two distinct but related areas, with two deliverables published in August 2026.

D48.3 defines the conceptual framework for the Self-Driving Freight Wagon (SDFW), covering use cases, system architecture, and a classification of five evolutive SDFW types that progressively integrate autonomous capabilities into rail freight operations. The deliverable validates use cases across two operational domains: Self-Shunting (yard automation) and Self-Driving (line operations), and clarifies interactions with key building blocks from related flagship projects, including the Yard Control System, Traffic Management System, Digital Automatic Coupler, and Self-Propelled Freight Wagon components. A preliminary CAPEX/OPEX assessment is included to support early business case development.

D8.3 addresses the safety dimension of advanced Automated Train Operation. It provides a safety analysis and risk assessment for the safety-critical functions of the Automated Driving Module, Automated Processing Module, and Repository, and defines updated safety requirements and SIL levels for ATO up to GoA4. The deliverable builds on the System Requirements Specification baseline 1 and updates hazard identification carried out in the predecessor X2R-4 project, with results intended to feed into the next version of the ATO GoA3/4 specification.

This deliverable analyses innovation pathways for the decarbonisation of railway operations, using Ukrainian Railways (UZ) as a case study. It examines the organisational structure supporting innovation, identifies the main areas of railway decarbonisation - including track electrification, alternative traction, and digitalisation - and assesses progress made in each area. 

The study identifies the main barriers slowing the implementation of decarbonisation measures, ranks them by impact and complexity, and proposes key conditions to accelerate innovation, with digitalisation identified as the highest priority. Medium-term scenarios for the future evolution of UZ are developed, concluding that a gradual and adaptive approach to modernisation is the most realistic pathway. While recognising the specific challenges created by the ongoing conflict in Ukraine, the report notes that many of its findings are relevant to broader European railway innovation practices.