Digitization of railways operations with high precision positioning

Accurate, robust, open and universal positioning for railways operations

Until recently radio navigation systems (GPS, Galileo, Glonass and BeiDou) were not able to offer the accuracy and the robustness required by the railway industry. However, thanks to Geoflex’s hypergeolocation this accuracy and reliability are now available and getting implemented in trains.

Available since 2018, Geoflex’s truly global GNSS correction service, eliminates most of GNSS inaccuracies to offer a decimeter level accuracy that meets the stringent requirements of the railway industry.

Moreover, Geoflex positioning engine embeds a sensor fusion platform which integrates easily with third party inertial sensors and tracks’ geometry data to enable continuous, high quality positioning under bridges, in tunnel and stations, wherever GNSS satellite’s signal reception is weak or obstructed.

Geoflex service is open and universal: it works with all GNSS receivers, it is fully compatible with GPS, Galileo, Glonass and BeiDou and its GNSS correction data are available in a standard industry format (RTCM). Furthermore, Geoflex offers not only a correction data stream but also, an optional software development kit and as well as a hardware box, to answer all customer’s requirements.

One system, multiple Benefits

High accuracy and robust GNSS positioning opens up a wealth of benefits for railways’ operations. In high density areas it allows significantly more capacity and higher speed on current infrastructure. It also reduces the high maintenance costs on low density lines where trackside signaling equipment is no longer required.

Precise train positioning also improves trackside personnel protection and brings additional, valuable information to assist train drivers within Driver Advisory Systems (DAS).

It brings real-time location of the train into the on-board passenger information system. Ultimately hypergeolocation creates additional value for asset management and predictive maintenance.