Digital Railway Operations: How Adaptive Train Guidance Increases Efficiency in the ÖBB Network

The Key Takeaways

• Adaptive Train Guidance (AZL) helps ÖBB reduce unscheduled stops and stabilize railway operations.
• Evolit supported this initiative with methodical project management and structured requirements engineering.
• Key tasks included managing a highly complex project schedule, applying the SFERA standard at the interface between TMS and vehicle communication, and providing technical and methodological support for core system and process extensions.
• The approach included a high-level data flow diagram, identification of systems to be modified or newly developed, and coordination of interface development between systems.
• Initial results show potential in punctuality, energy savings, and operational efficiency.

Digitization is fundamentally transforming railway operations. It increases transparency on all levels by interconnecting systems—from the locomotive to the dispatching system and signal control—enabling data to flow more efficiently, be stored in a structured manner, and be evaluated with AI support.

With its “Digital Railway Operations” program, ÖBB-Infrastruktur AG—one of the largest rail infrastructure operators in Europe—has sent a strong signal for the future of rail transport. A key initiative within this program is the large-scale project Adaptive Train Guidance (AZL).

Evolit had the opportunity to support the three-member project management team and the corresponding technology managers from the “Train Control” and “Digital Services” division for a total of six years, helping lay the foundation for improved punctuality, energy efficiency, and more stable railway operations.

Initial Situation & Challenges: When a Single Stop Affects the Entire Network

In day-to-day operations, situations arise where trains come to an unscheduled stop. While this may seem harmless on the level of a single train, it can trigger a domino effect across the network: if a train stops in a block section, subsequent trains must be rerouted to avoid delays, for example by initiating bi-directional track operations or establishing alternative routes.

For ÖBB, this meant resolving conflicts in train paths as early and efficiently as possible to ensure stable and safe operations.

The AZL project’s goals were accordingly ambitious: At the newly developed Train Journey Management Interface (ZFM-SST), the system had to provide a train-specific timetable, digital operational speed restrictions (operational LAs), and real-time driving recommendations (AZL recommendations). This required solutions that would integrate seamlessly into existing systems while meeting the technological demands of a modern railway network.

A particular technical challenge was implementing a new interface according to the SFERA standard between the Traffic Management System (ARAMIS) and the trains, as well as establishing an ordering process that required collaboration among stakeholders with differing interests.

This multi-layered large-scale project was embedded in the broader “Digital Railway Operations” program, involving numerous other stakeholders, parallel development tracks, and a high degree of coordination due to continuously evolving communication plans.

To succeed in this context, disciplined, structured, forward-thinking, and expert project support was essential—requiring strong IT expertise and a sound understanding of railway infrastructure.

Analysis & Approach: From the Big Picture to Technical Implementation

The multitude of goals, systems, and requirements necessitated a clear and consistent project structure from the outset. This structure was mapped in MS Project, Confluence, and Jira and kept synchronized across all systems. During the course of the project, it had to be adjusted several times—directly impacting all planning foundations, especially the highly complex and interconnected project schedule.

Evolit provided continuous and comprehensive support to the AZL project management team in handling this demanding task.

Together, we analyzed existing processes and identified the work packages, deliverables, and dependencies that were critical to the project’s success. This created a shared understanding of structure, timeline, communication, risk planning, and scope management. Especially in a project of this scale, it was crucial to define the scope clearly while remaining flexible, as requirements evolved over time.

Building on this, we developed a methodological requirements structure that intentionally operated on multiple levels. From the overarching project goals, we first derived target processes at an abstract overview level to make the overall workflow transparent. Only then did we move to detailed design: use cases were specified, functional requirements were defined, and based on those, we determined which existing systems needed to be extended or which new systems had to be developed.

Key technical tasks also included defining and managing the ordering process in M-AMA and the associated interfaces—including one for a token service enabling railway undertakings (RUs) to retrieve driving recommendations. Technical implementation was coordinated across multiple teams, communication interfaces were clearly defined, and dependencies were kept consistently transparent.

The greatest challenge lay in the high technical complexity: numerous systems had to communicate with each other, new interfaces had to be designed, and all elements had to be embedded into a comprehensive integration architecture. A detailed data flow diagram served as a common reference and made the functional logic comprehensible for all stakeholders.

Thanks to this structured and multi-level approach, we were able to manage the complexity and advance the project in a targeted manner.

Results & Benefits: More Efficient Operations, Lower Energy Consumption, Greater Stability

With Adaptive Train Guidance, ÖBB is laying the foundation for smarter and more modern railway operations. The first effects are already tangible:

• More stable operations, fewer conflicts in train paths, and smoother, more energy-efficient driving behavior.
• Predictive driving recommendations reduce unscheduled stops, which has a direct positive impact on punctuality.
• Energy efficiency also improves: When trains brake and accelerate less abruptly, energy consumption decreases—an important signal toward sustainable transport systems.

The collaboration within the AZL project demonstrates just how complex modern train control can be—and how crucial clear structure, technical expertise, and solid stakeholder management are. With our expertise in project management, requirements engineering, and the support of complex infrastructure projects, we were able to help shape a process that will offer substantial added value to railway operations in Austria.