The Future of Railway

Author

RailPulse Tech
January 15, 2025


The European Union Railway Agency (ERA) has recently released its Rolling Stock Fleet Study, offering a comprehensive analysis of Europe’s rail fleet.

Drawing on ERA registers, open sources, and industry data, the report provides critical insights into the size and characteristics of the fleet, supporting innovation and operational improvements across the sector.

The study identifies approximately 835,000 registered vehicles as of mid-2024, including

  • 130,000 traction vehicles

  • 635,000 wagons

  • 45,000 coaches and

  • 18,000 special vehicles.

This data fills key knowledge gaps and informs strategies to meet Europe’s goal of doubling rail freight and tripling high-speed passenger transport by 2050.

Filling the Gaps 
ERA’s Analysis of Europe’s Rail Fleet and Future Needs

ERA uses three registers -- European Vehicle Register (EVR), European Register of Authorised Types of Vehicles (ERATV), and Vehicle Keeper Marking (VKM) register -- to manage rail vehicle data. However, gaps in data quality, incomplete coverage (only 23% of vehicles have an ERATV type ID), and limited analytical design hinder interoperability analysis and innovation projects like Digital Automatic Coupling (DAC) and European Rail Traffic Management System (ERTMS).

To address this, ERA conducted a study to enrich datasets, analyse fleet characteristics, and identify future needs. The findings aim to improve data quality and support a more interoperable and innovative EU rail system.

Key Findings

  • Silent Wagons: European Vehicle Register (EVR) data doesn’t fully cover silent wagons; many wagons have been upgraded with silent brakes.

  • Agricultural Wagons: Data on grain wagons varies; more operational research is needed.

  • Intermodal Wagons: 105,000 intermodal wagons were identified, but we need to clarify how many are actually used for this purpose.

  • Power Systems: Current power system data is inconsistent; classification needs updating.

  • Electrification: Multi-system vehicles, capable of running on different electrification types, are common.

  • Tractive Effort: Data on 40,000 vehicles was gathered, but its use for statistics is debatable.

  • Trainsets: About 13,100 trainsets were identified through EVN data.

  • Seating Capacity: Focus should shift from trainset-level seating to individual cars.

  • ETCS: Incomplete data on the European Train Control System (ETCS); more details needed by linking with specific vehicle identifiers.

  • Vehicle Keepers: More wagons are now owned by lessors than operators.

  • Rolling Stock Needs: Initial data suggests further analysis is needed to estimate future needs by 2050.

  • Key Observations
    The study reveals critical insights and challenges in analysing EU rail fleet data:

  • Open-Source Data: Significant vehicle data is publicly available, despite restrictions under the European Vehicle Register (EVR) Decision[DD1] . Parallel registers also exist, but data governance remains inconsistent.

  • Enhanced Insights: Custom scripts unlocked ERA register data, creating enriched datasets to support policy and retrofitting projects like DAC and ETCS. The study also identified areas for improving data quality.

  • Data Limitations: Open-Source data [DD2] is incomplete, time-consuming processing, and lacks signalling information. Rail data lags behind aviation and maritime sectors, where open platforms like Flightradar24 and MarineTraffic provide integrated insights.

  • Future Solutions: Linking EVN to ERATV IDs will improve data for new vehicles. For legacy fleets, alternative methods are needed to close gaps.

  • Sectoral Lessons: Better data availability and integration, as seen in other transport sectors, could drive innovation and enhance rail system interoperability.

ERA Proposals

The study recommends that ERA improve technical data for vehicles lacking ERATV IDs, use enriched datasets for better impact assessments, and enhance data quality in registers. It also suggests streamlining EVR restriction code updates, refining ERA’s Railway Factsheets, and correcting EVN-based errors for the statistical office of the European Union (EUROSTAT).

By addressing these data gaps and enhancing interoperability, ERA’s study lays the foundation for a more connected, efficient, and sustainable European rail network, supporting the sector’s ambitious growth targets for 2050.

Innovating Rail 
The Role of Advanced Technologies in Overcoming Industry Challenges

The railway industry is undergoing a profound transformation, driven by emerging technologies that are revolutionising infrastructure design, operations, and maintenance. Traditional challenges such as ageing infrastructure, safety concerns, and escalating operational costs are being tackled through innovations in 3D printing, AI, digital twin solutions, and robotics.

Additionally, the strategic implementation of big data and advanced analytics, coupled with enhanced cybersecurity measures and clean technology solutions, is helping the industry streamline resources while minimizing its environmental footprint.
Cloud computing, enhanced connectivity, and the Internet of Things (IoT) further boost communication and operational efficiency.

7 Emerging Technologies Impacting the Future of Railway

Additive manufacturing plays a pivotal role in revolutionizing railway operations. Through technologies like FDM, SLA, and SLM, rail companies can produce custom spare parts, reduce material waste, and improve production efficiency.

This results in lower operational costs, and quicker maintenance, and contributes to sustainability by minimizing energy consumption and cutting waste.

●      Use Cases:

○  Custom Spare Parts Production: Produces parts on demand, reducing inventory costs.

○      Lightweight Components Manufacturing: Creates durable parts like brackets from carbon fiber and 3D printing technologies, improving fuel efficiency.

○      Obsolete Components Fabrication: Facilitates the production of hard-to-find parts, helping maintain ageing infrastructure. 

AI is reshaping the railway industry by leveraging sensor data and advanced algorithms to predict failures, optimize schedules, and automate maintenance.

Neural networks detect faults, while machine learning helps refine operational strategies.

●      Use Cases:

○      Real-Time Fault Prediction: Identifies issues early through sensor data analysis.

○      Optimized Scheduling: Enhances train scheduling through machine learning models, improving reliability and reducing delays.

○      Automated Inspections: Utilizes AI, drones, and robots for fast, accurate infrastructure inspections.

Digital twins are revolutionizing asset management in the railway sector by creating virtual models of trains, tracks, and stations.

This real-time digital representation helps optimize performance, schedule maintenance, and improve safety by simulating real-world conditions.

●      Use Cases:

○      Reducing Downtime: Monitors train components to predict and prevent breakdowns.

○      Infrastructure Management: Uses digital twins to streamline the planning and maintenance of rail infrastructure.

○      Optimized Rail Scheduling: Simulates different scenarios to fine-tune train schedules for efficiency.

Big data tools and analytics platforms process vast amounts of data from sensors and tracking systems to enhance decision-making, reduce downtime, and improve operational efficiency.

●      Use Cases:

○      Preemptive Equipment Management: Predicts failures through IoT sensor data analysis, enabling early maintenance.

○      Real-Time Operation Monitoring: Enhances the management of delays and equipment malfunctions.

○      Passenger Experience: Uses passenger data to improve services, optimize pricing, and enhance satisfaction.

With the increasing integration of IoT, AI, and cloud technologies, cybersecurity is crucial in protecting critical infrastructure and maintaining operational integrity.

It ensures that sensitive data, such as passenger information and operational data, remain secure.

●      Use Cases:

○      Protection of Control Systems: Firewalls and intrusion detection systems safeguard control systems from cyberattacks.

○      Passenger Data Security: Encryption and multi-factor authentication secure passenger information.

○      Real-Time Threat Detection: Identifies cyber threats quickly using AI and anomaly detection systems.

Clean technologies are addressing the railway industry's environmental impact by introducing zero-emission trains and energy-efficient systems.

This includes innovations like hydrogen fuel cells, regenerative braking systems, and solar-powered rail infrastructure.

●      Use Cases:

○      Hydrogen Fuel Cell Trains: These trains use hydrogen to generate electricity, emitting only water vapour.

○      Regenerative Braking Systems: Captures and stores energy during braking, reducing energy consumption.

○      Solar-powered Rail Infrastructure: Powers stations and signalling systems using solar energy, reducing reliance on traditional power grids.

Cloud platforms enable rail operators to manage, analyze, and store large data sets efficiently.

By moving infrastructure to the cloud, rail companies reduce the need for on-premises hardware, improve operational resilience, and enable better collaboration.

●      Use Cases:

○      Disaster Recovery: Ensures data is backed up and can be restored in case of failures or cyberattacks.

○      Ticketing and Reservation Systems: Scalable platforms improve performance during high-traffic times and offer personalized experiences.

○      Real-Time Train Monitoring: Uses cloud systems to track train locations and optimize scheduling.

EU Fund
€350 Million EU-Funded Rail Project Boosts Croatia-Hungary Connectivity

Croatia has partially opened a €350 million rail upgrade, co-funded by the EU, to boost freight and passenger connectivity with Hungary.

The project includes a 15-kilometre dual-track section between Koprivnica (Croatia) and Gyékényes (Hungary), as well as a new Drava River bridge. Expected to be completed by the end of 2025, the upgrade will improve cross-border transportation and reduce bottlenecks.

The railway section, part of the "Križevci–Koprivnica–State Border" project, features a double-track bridge over the Drava River and upgraded signaling systems.

Once finished, the line will support speeds up to 160 km/h and handle 30 freight trains daily—markedly increasing traffic from the Port of Rijeka to Hungary. The project is 75% complete, with work continuing on the section between Mučna Reka and Koprivnica.

The new 300-meter Drava Bridge, constructed using a unique longitudinal push technique, replaces a single-track bridge and increases capacity for modern freight.

The project is part of HŽ Infrastruktura’s €6 billion rail modernization plan, aimed at enhancing key corridors like Rijeka-Zagreb-Budapest and Slovenia-Zagreb-Serbia to support economic growth and regional trade.

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