With the European Green Deal, the European Union intends to hugely expand the amount of freight transported by rail from just 18% today to 30% by 2030. But for this to happen, rail freight must become much more competitive. To make the most of the natural limitations of rail infrastructure, rail freight must consume less energy, operate more punctually, and flow more smoothly.
The LEADER (Locomotive Engineer Assist Display & Event Recorder) Driver Advisory System is one of the key enablers of this industry-wide revolution. Using information on the train’s configuration, route, timetable, current speed and geolocated position, the s-DAS (standalone Driver Advisory System) works out the most efficient way to drive at any given moment. Ideally, live updates on current traffic conditions should also contribute to this optimization (c-DAS – connected Driver Advisory System). The resulting recommendations appear on a display in the driver’s cab.
Knorr-Bremse is – as it were – predestined to create such a system. The company’s developers have been working on Driver Assist and Advisory Systems since 1996, making it one of the very first pioneers in the field. As an established supplier of systems to the rail industry, Knorr-Bremse is a resilient partner capable of guaranteeing the successful implementation – based on the company’s advanced skills in onboard integration – of the product version intended for permanent installation in the driver’s cab. But Knorr-Bremse also partners with the various organizations responsible for national infrastructure, collaborating on the provision of input data and live updates from trackside operations.
As well as excelling in collaborative partnerships, a key factor in the company’s success, Knorr-Bremse also believes that the inclusion of data on energy consumption (energy data) captured from onboard metering systems should form part of the product philosophy behind successful, long-term DAS implementations. For operators striving for sustainable energy management, the transparent depiction of their operational energy consumption is the ultimate way to close the circle.
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Multiple deployment options thanks to three product versions
To ensure that LEADER is deployed across as much of the European rail network as possible, the system is available in multiple versions.
LEADER4DiLoc and LEADER4Tablet deliver driving recommendations to the vehicle operator’s mobile devices. The actual algorithms “run” in Knorr-Bremse’s cloud environment (which is, by the way, carbon-neutral). The results are delivered to the mobile devices over the cellular network. In the case of the built-in LEADER solution installed in the driver’s cab, drive recommendations are computed on the panel PC itself. User logins, downloads of travel data to the panel PC, and transfers of relevant statistics to the back office and cloud, all happen also via the cellular network.
All product versions use the web-based back office. Not only does it act as a “terrestrial” interface between the – carbon-neutral – cloud backend and the live updates from trackside operations. It also provides a comprehensive (real-time) monitoring and analysis tool that makes it possible to use the system on the track.
Driver Advisory Systems as precursors of automated train operation (ATO)
There is still a long way to go until automated train operation (ATO) becomes widespread on Europe’s open rail networks. Even so, Driver Advisory Systems such as LEADER are playing a pioneering role: Driving profile optimization algorithms that take account of vehicle characteristics, the train’s timetable, track topography and real-time data on surrounding traffic conditions represent a key architectural component of automated train operations capable of achieving the highest possible levels of energy efficiency.
In Australia, Knorr-Bremse’s LEADER Auto Pilot variant is making a significant contribution to the world’s first end-to-end ATO application in the freight-train space. The Rio Tinto mining corporation is using the system to provide advanced, energy-efficient driving strategies, as well as precisely control their trains’ self-driving capabilities.