As Europe’s railways expand, their safety and efficiency depend increasingly on knowing every train’s position....
Satellite positioning set to enhance train localisation
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Context-driven Dynamic Human-Machine-Interface Design and Prototype
Description: This deliverable reports on the development of a human-machine-interface (HMI) enabling the user to access machine learning models completely by assessing their input, output, and the model itself and what is driving its decisions. This allows the user to better understand and eventually steer the model efficiently and effectively to produce overall better outcomes and improve the decision-making process. In the deliverable, the architecture and software components are described. The HMI is detailed in four scenarios, pointing out the use cases and identifying the differences between the scenarios.
Target audience: Infrastructure Managers
How it brings us closer to achieving better rail for Europe: The output of the research contributes to the overall Intelligent Asset Management System (IAMS) concept in the railways context, which shall enable the implementation of prescriptive analytics, contributing to achieving an efficient, safe and intelligent infrastructure maintenance approach.
More information on this topic: DAYDREAMS