Story | 10/27/2026
A measurement reveals how a track is performing today. A forecast can help infrastructure managers decide what to do tomorrow. Connecting these two is a key development in rail infrastructure: integrating physical assets, inspection data, engineering expertise, service excellence and digital intelligence to build future-proof infrastructure through a holistic, predictive approach.
At InnoTrans, this development took center stage in Vossloh’s exhibition area. Digital solutions were presented as a connecting element between components, systems and services among different fields of application. The strong interest from visitors suggested that digital tools and services are becoming increasingly mature and relevant to everyday operational decisions.
The results of Vossloh’s recently conducted customer survey reinforce that impression. The findings offer an early view of what customers need: better insight into asset condition, less manual effort and practical support in planning maintenance before problems disrupt operations.
For infrastructure managers, digitalization is closely connected to very physical challenges. In the survey, 60% of respondents identify aging infrastructure as a major challenge, followed by maintenance backlogs at 51% and workforce shortages at 50%.
These pressures make it increasingly important to understand where intervention is needed most and where available resources can deliver the greatest benefit.
The customer priorities are clear: More than 90% of respondents consider advanced inspection technologies important, while nearly 90% emphasize the value of detailed, automated inspection data. The strongest demand for specific solutions concerns automated defect detection and classification, selected by 61%, and real-time condition monitoring of track and turnout assets, selected by 53%.
The message is not simply “collect more data”, it is rather turning data into decisions that help maintain a reliable railway.
This distinction matters. Detecting an unusual vibration or a change in track geometry provides valuable information about current condition. Following that change over time and interpreting it alongside operating conditions and maintenance history can create the basis for forecasting deterioration or identifying an emerging failure risk. Research into railway predictive maintenance describes two central objectives: predicting failures and estimating the remaining useful life of assets. It also highlights the importance of connecting forecasts with inspection and maintenance planning, rather than treating analytics as an end in itself.
Customers see considerable potential in this direction: 65% of survey respondents identify predictive maintenance using AI and machine learning as one of the technologies likely to have the greatest impact on the rail industry in the coming years. For infrastructure managers, the potential benefit is practical: more time to prepare interventions, better prioritization of maintenance work and fewer decisions made under the pressure of an unexpected disruption.
The world premiere of Vossloh’s TrackStability software attracted strong recognition at InnoTrans. Its presentation illustrated the increasingly important principle that progress toward prediction depends on bringing hardware, data, services and external information together.
Sensors and inspection technologies provide observations. Knowledge of components and track systems helps explain them. Maintenance records add the history of previous interventions and external information can provide further context.
Weather is one example. Temperature, rainfall and other environmental conditions can help explain changes in asset behavior. Operational information, such as traffic and loading, adds another dimension. Railway analytics providers already describe approaches that combine wayside and onboard measurements with maintenance records and environmental information to support predictive insights.
The significance lies in the combination. A measurement becomes more useful when its context is understood; a forecast becomes more actionable when it is connected to engineering judgment and the ability to carry out maintenance.
Vossloh’s Smart Turnout solution illustrates this connection in practice. Monitoring vibration and displacement helps users follow degradation, plan interventions and check whether maintenance has restored nominal condition.
The survey also makes clear that technical capability alone will not determine adoption.
Proven references matter to 88% of respondents, and seamless integration into existing systems to 86%. Scalability, cybersecurity and data sovereignty are also major purchasing criteria. Data ownership is particularly important: around 88% consider clear ownership essential, while 86% expect long-term access to their data regardless of vendor relationships.
For predictive solutions, these expectations are fundamental. Customers need confidence not only in how their information is handled, but also in how forecasts are produced and used. Research highlights explainability, data quality and operationally meaningful evaluation, including warning lead time and false alarms, as important challenges for railway predictive maintenance.
Obviously, the discussion about digital solutions in rail infrastructure is not about “software replacing hardware or expertise”. The next chapter is about making them work better together: from inspecting assets, to understanding their condition, to anticipating their needs—and turning that insight into timely action.