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Siemens Mobility’s AI-Enabled Digital Twin Signals New Approach to Rail Modernization

To maintain reliability and safety, many of the rail networks of Europe are undergoing a massive digital modernization that includes digital twins. Backed by artificial intelligence, these digital representations of rail systems are helping facilitate the effective management of signals and inventory across the entire asset lifecycle—and Siemens Mobility is leading the way.

The Trimble MX9 is the first vehicle-independent mobile mapping system officially approved by Deutsche Bahn for surveying tracks, points clearances and topographic objects.

The organization’s building information modeling (BIM) for rail infrastructure efforts encompass the full digitalization of the planning, construction and operation of rail assets, including signaling and structures along all of the lines. The skilled technicians at Siemens Mobility are putting its digital twin techniques to work for Germany's Deutsche Bahn-initiated €12.7 billion ($15.3 billion USD) program to modernize its rail network. 

Minimal Disruption with Maximized Results

Integral to Deutsche Bahn’s rail network modernization efforts is the move to the European Train Control System (ETCS), the advanced signaling and control component of the European Rail Traffic Management System (ERTMS). The first component of the BIM modernization process is documenting and digitizing the signaling infrastructure of the Deutsche Bahn network.

Siemens Mobility GmbH is supporting Deutsche Bahn to facilitate infrastructure capture of its inventory. Carolin Baier, head of Digital Twin & Digitalisation Domestic Market Germany for Siemens Mobility, said, “Deutsche Bahn recognized early on that before they could deploy ETCS, they needed valid data of the tracks. Our job is to reset or reboot the infrastructure inventory in different projects. That means we really need a clearer picture.”

The digital track capturing process provides rich data for digital twin creation. Image courtesy: Siemens Mobility

Outdated as-built information in an inventory doesn't allow for accurate planning. Especially in large infrastructure projects getting up-to-date data and information using traditional surveying methods is tedious and can cause major delays.

In the past, this task would have required a survey team to measure using track wheels, using mostly outdated paper plans. 

Accuracy Matters

For Siemens Mobility, the process of data collection is built around a mobile mapping system that can be installed on trains, trolleys and cars, as needed. Baier added, “It’s quite important that we are as accurate as 2 centimeters, in some cases. That specification guides the choice of collection method, in this case our high-precision surveying system.”

Siemens Mobility relies on the vehicle-mounted Trimble® MX9 mobile mapping solution with dual head laser and the AP60 IMU, the highest-grade IMU on the market. Trimble’s MX9 is the first ever vehicle independent mobile mapping system officially approved by German national railway company Deutsche Bahn for surveying tracks, points, clearances, and topographic objects. The scanner is mounted on the train for streamlined data gathering. 

 “The great thing about this mobile mapping solution is that we can gather information at normal train operation speeds, usually between 80 and 110 kilometers per hour, and avoid business interruptions,” Baier continued. 

Siemens Mobility piloted the mobile mapping system in the Deutsche Bahn Netz AG Region East as part of an as-built data upgrade project. The resulting georeferenced 3D point cloud of the rail infrastructure included detailed images of the track, the track environment and the track gradient. 

Skilled technicians at Siemens Mobility are putting digital twin techniques to work for Germany's Deutsche Bahn rail network modernization program. Image courtesy: Siemens Mobility

Algorithmic Advantage

Besides the point cloud data and images from the MX9 mobile mapping system, the Siemens Mobility team must incorporate a considerable amount of customer data from various other sources, such as geographical information systems (GIS) and the real estate register (ALKIS), into the digital 3D model.  

All of this data is synchronized to create an as-built BIM model that is positioned within a global georeferenced coordinate system—in essence a digital twin of existing rail network and associated infrastructure (e.g., signals). The Siemens team then performs a gap analysis to see if the objects in the customer database match the positions in the point cloud.

The data collection process is built around a Trimble MX9 mobile mapping system that can be installed on trains, trolleys, and cars, as needed. Image courtesy: Siemens Mobility

The next step is to identify and extract system objects and to implement a complete system layout in the form of a scaled track layout. For instance, on a 500-800 kilometer segment, there might be around 120,000 different objects. 

“Rich data is great, very detailed and thanks to mobile mapping, we can capture it quickly. But AI is really the key to unlocking the power of data because it allows us to work with these large datasets in an efficient way,” Baier added.

Working with the Siemens Mobility AI group, Baier and her team developed customized algorithms for specific use cases, such as automatic object recognition of track elements.  

Baier continued, “Object detection and AI algorithms support current and future developments, allowing rail infrastructure optimization as well as train movement in semi or fully autonomous systems. Without AI technologies to help sort out data, we could not develop these future systems.”