Pecovasa introduces geofencing system for vehicle transport wagons

Freight train carrying multiple cars inside Pecovasa vehicle transport wagons with protective metal mesh sides
© Renfe
The system integrates IoT sensors, a mobile application, and artificial intelligence features, with a total project cost of EUR 590,000.

Pecovasa, a subsidiary of Renfe Group focused on rail-based vehicle logistics, has launched a digital system aimed at enabling real-time geolocation and tracking of its fleet of car transport wagons. The initiative has been partially financed through EU Next Generation funds, with a contribution of EUR 180,000.

  350 wagons equipped with IoT devices  

The new system has been installed on over 350 wagons and is designed to support monthly operations involving more than 25,000 vehicles. The sensor-equipped wagons now transmit live GPS coordinates, as well as environmental and technical metrics such as temperature, humidity, and acceleration. Data transmission frequencies and other settings can be configured remotely.

The solution also enables wagon geolocation within terminal facilities, allowing for more precise yard management. Tracking of distance travelled helps improve oversight of maintenance schedules and enables condition-based intervention. The system's sensors provide immediate feedback in cases of abnormal operation or recurring issues.

  Application for customers and operations management  

An accompanying application developed by Pecovasa offers access to tracking data and route history. Users can view trip details, wagon status, and loading/unloading points. Search functions by chassis number allow customers to identify their shipments and check their current location during transit.

The app further integrates with operational systems, supplying logistics staff with real-time data to coordinate traffic and manage exceptions.

  AI used for predictive insights  

Data collected by the system is analysed through AI-based algorithms. This allows for identification of parts exposed to mechanical stress or abnormal vibrations and contributes to predictive diagnostics. The system can also be applied in process modelling, scenario analysis, and long-term fleet optimisation.


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