• For the first time in Europe, Nokia Scene Analytics solution is deployed by BasellandTransport AG  in Münchenstein,Switzerland.
• AI-based solution improves the safety of railroad crossings with real-time monitoring, using machine learning algorithms to continually increase accuracy and response, improving operational and cost efficiencies.

Nokia announced the deployment of its Scene Analytics solution forBaselland Transport AG in Münchenstein, Switzerland. The AI-based system applies computer vision and machine learning technologies for real-time monitoring and analysis, to ensure the safety of railroad crossings. As the first deployment of this kind in Europe, Nokia’s collaboration with Schweizer Electronics and BLT demonstrated the reliability of AI-based railroad safety solutions for daily use.

Safety of passengers and vehicles at level crossings remains a concern for rail authorities due to the threat of serious injury or loss of life in these areas. Statistics from the European Union identified around 250 fatalities and 300 serious injuries related to level crossings in the EU-28 countries in 2018^[1]. Even the best warning systems can be bypassed, and crossings obstructed, making it essential for train operators to be alerted of issues in real-time.

Integrating Nokia Scene Analytics, BLT can use machine learning algorithms based on CCTV data to continually learn what is “normal” or anomalous. In addition to reporting anomalies to railway security in real-time, the AI-based platform detects the object type, which provides a more complete picture of the situation at hand. Event based video clips, images and associated data are stored, enabling post-incident forensic analysis.

Besides improving safety and response time, the deployment of Scene Analytics on railroad crossings also increases operational efficiencies by minimizing downtime and delays. Its machine learning capability reduces the time investment required by rail personnel to manually update the system. In doing so, Nokia Scene Analytics provides train operators with much greater overall cost efficiency. It can also be integrated with many standard industry cameras, reducing the total cost of ownership, and increasing the return on investment.

Michael Theiler, Head of Maintenance Electrical Systems at BLT, said: “Level crossings are notoriously difficult areas to ensure the safety of passengers, pedestrians, train operators and motorists. This deployment, in collaboration with Nokia represents an encouraging step towards using analytics as another layer of protection in dangerous areas. Nokia Scene Analytics acts as an intelligent set of ‘eyes’ and, by providing critical information in real-time, to prevent or mitigate the impact of an incident”.

Roland Liem, Head of Product Unit Railroad Safety at Schweizer Electronics, said: “By combining level crossing systems and Scene Analytics within a simple interface, this project with Nokia and BLT enabled us to automate the interaction between level crossing systems and alarms for enhanced safety. This will enable rail operators to close barriers and respond to dangerous situations at crossings in real-time.”

Karsten Oberle, Head of Rail at Nokia, said: “As the first deployment of its kind in Europe, this project with Schweizer Electronics and BLT enabled us to address many of the level crossing safety issues which are at the top of priority lists for rail operators. It is now our ambition for Nokia Scene Analytics to become a key part of the transition towards the digitalization of future railways. By integrating machine learning into level crossing systems, we will be able to continuously improve and refine safety processes in real-time. This will ensure that safety remains at the forefront for train operators, workers and customers alike.”