Digitalization
Digitalization is a key success factor for the transformation toward a robust, powerful, state-of-the-art railway for our customers and employees. For greater quality, productivity, comfort and performance, we need a networked interaction in a digital overall system.
Digital Rail for Germany
Digitalization, automation and artificial intelligence are the key to greater capacity and an optimal utilization of the rail network. The vision of Digital Rail for Germany (Digitale Schiene Deutschland; DSD) comprises a digital, highly automated rail system. Digital interlockings (DSTW), the European Train Control System (ETCS) and Automatic Train Operation (ATO) form the basis of this vision.
Stuttgart will be the first region in Germany to implement digital train control and interlocking technology – but expectedly one year later than planned in 2026. To this end, all components of the track infrastructure, the future station, the Stuttgart digital hub (Digitaler Knoten Stuttgart; DKS) and the vehicles will be intensively tested and the operating staff carefully prepared for this. However, equipping the track infrastructure alone is not enough. In order to support the corresponding refitting of vehicles, the BMDV published a vehicle funding guideline for the affected vehicles in the DKS, so that the vehicle equipment process (regional transport and S-Bahn (metro) multiple units) started in 2022 and is currently ongoing. A total of 333 vehicles are being retrofitted for ETCS and ATO.
Important fundamentals and prerequisites for ETCS use are digital and electronic interlockings. For example, DSTW Meitingen-Mertingen has commenced operations and replaced two interlockings that are more than 60 years old. It is the first digital interlocking on a high-speed line in regional and long-distance transport with line speeds of up to 200 km/h. A prerequisite for commissioning was the construction of a technology site (Technikstandort; TSO) in Donauwörth. In addition, preparatory work on the command and control technology for the future equipping of the line with ETCS was carried out on one of the busiest lines in Germany, between Halle/Leipzig and Berlin. In May 2024, the Bühl electronic interlocking (ESTW) was put into operation. The Bühl ESTW is the seventh interlocking of its kind on the section of the line between Karlsruhe and Basel and is an important prerequisite for the future equipping of this route with ETCS.
Work on automated driving will continue in a follow-up project to the Sensors 4 Rail project, which was completed in 2023. Under the name Automated Train, a fully automated, driverless system for provisioning and stabling runs will be developed for the first time within the next three years, which will result in a more flexible use of trains in the rail system of the future. Moreover, sensor-based obstacle detection will be used to demonstrate an intervention in the vehicle control, i.e. by using intelligent software combined with sensors at the front of the train, it will be able to recognize the environment and react independently to obstacles, i.e. to brake.
DSD is a major lever for driving Germany’s transition to more sustainable mobility. The corresponding ETCS fitting strategy, until 2029, was opened to the sector for consultation by DB InfraGO AG in the spring as part of the 2024 Network Terms of Use (NBN). The published content provides details about the planned equipping of the lines with ETCS, ATO and the Future Railway Mobile Communication System (FRMCS). Simultaneously, the class-B systems intermittent automatic train control (PZB) and the continuous train control system (LZB) for DB InfraGO lines will be decommissioned by December 2029. The published line-specific commissioning and decommissioning data are, however, subject to the reservation that the necessary means of financing are provided in the Federal budget.
Digital transformation
Our vision is to create a digital railway. This means using integrated and holistic digitalization to bring about a networked overall system within the Integrated Rail System. Selected projects helping to bring us closer to achieving this goal, taking the vehicle maintenance as an example, include:
- The digitalization of the fleets to enable automated capturing and monitoring of condition data. For example, vehicles continuously send condition data while in operation or when passing through fixed checkpoints to enable fault prediction and predictive maintenance.
- The supporting of the fleet management process by AI to achieve optimal planning and management of maintenance stops. Thereby the capacity of the depots is optimized and maintenance measures are prioritized. This happens through digital fleet management.
- Automation and digitalization in the depots to enable efficient maintenance. Here, the main focus within the depots is reducing the amount of manual planning that has to be done, automating repetitive tasks and creating free track capacities.
With initiatives such as digital maintenance and the use of artificial intelligence (AI) in the rail system, we are mutually forging ahead with the digitalization of the railway and joining forces in the use of new technologies to achieve a sustainable increase in capacity, quality and punctuality. Digitalization also counteracts impending shortage of skilled labor.
Artificial intelligence in rail operations
DB Group is expanding the use of AI for the scheduling of trains. Rail operations are to become more punctual thanks to an application developed in-house. The program supports dispatchers in efficiently controlling traffic and avoiding delays. The application is being trialed on the S-Bahn (metro) trains in Stuttgart, Rhein-Main and Munich. In addition, its functionality is set to be expanded significantly to include the ability to integrate infrastructure constraints. Dispatchers thus receive support in the event of short-term route closures in the form of a single-track replacement service. The application is also currently being tested on the line between Mannheim and Basel. In this section, the AI system is operating for the first time outside a closed S-Bahn (metro) train system and must therefore handle different kinds of traffic, namely cargo, regional and long-distance trains. If this piloting stage is successful, the next step will see the system used on further highly utilized lines. DB Cargo is continuing to test a diagnostic service for freight cars using AI software. High-resolution images from 13 camera bridges at eight DB Cargo locations provide information on damage and load securing on freight trains. This allows irregularities to be rectified at an early stage and more freight cars to be put into service. As condition-based and predictive maintenance come into increasing use, AI-supported approaches to the intelligent combining of predictive maintenance planning and rail operations are being scrutinized.