Dear reader,

Digitalization and the increasing use of data-based methods are opening up far-reaching opportunities for rail transport. From maintenance and capacity planning to the control of infrastructure processes, it is clear that data intelligence has become a key lever for efficiency, safety, and customer satisfaction.

One particularly relevant field is predictive maintenance. With the help of sensor technology and artificial intelligence, the condition of vehicles and systems can be monitored in real time and deviations detected at an early stage. Operators such as Indian Railways and European metro systems are already using predictive maintenance solutions that forecast failures with a high degree of accuracy, thereby avoiding unplanned downtime. In addition to greater safety, this also reduces costs and makes maintenance intervals more efficient. In Germany, projects such as DEFLECTOR and D4M (BTU Cottbus-Senftenberg, Fraunhofer IKTS) are researching the targeted use of sensor technology and AI to make maintenance more predictable and resource-efficient.

Data usage is also opening up new perspectives in the area of capacity planning. Historical passenger numbers, real-time information, and demand forecasts serve as the basis for optimized timetables that improve both punctuality and utilization. The integration of different data sources – from reservation systems to video data – also creates transparency for resource management and operational decisions. However, this requires systematic harmonization of data to ensure a valid basis for decision-making. In addition, new applications are emerging in the field of automated driving: The enableATO project at RailCampus OWL, for example, is investigating how sensor technology, AI, and automated control can be used in rural regions.

Furthermore, data-based analysis enhances safety in rail operations. Sensors measure vibrations, temperature, or brake conditions so that potential hazards can be identified and remedied before they lead to disruptions. AI-supported processes can significantly reduce maintenance costs and measurably increase punctuality. At the same time, data-driven systems also support infrastructure control. Modern train control systems such as CBTC enable denser train sequences with consistent safety through precise position detection, while standardized data formats such as RailML improve interoperability between different IT systems.

However, these developments also come with challenges. Data protection and IT security are essential, especially when processing passengers' personal data. In addition, automation and data-based processes are changing traditional tasks in rail operations, which requires new training measures for employees. The quality and diversity of the data used is also crucial: reliable forecasts and decisions can only be derived from a broad and valid database.

Overall, it is clear that data-driven methods in rail transport have long since moved beyond pilot projects and are already having a noticeable impact on maintenance, planning, and operations. They make an important contribution to greater efficiency, safety, and sustainability, but at the same time require clear framework conditions and accompanying measures. The future viability of rail transport therefore depends not only on technical implementation, but also on responsible integration into organizational, regulatory, and social structures.

Read you next month!

Best regards,

Your RMR-Team