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RSSB TO USE ARTIFICIAL INTELLIGENCE TO HELP REDUCE TRAIN DELAYS CAUSED BY 'LEAVES ON THE LINE'
RSSB will use artificial intelligence and data analysis to predict and identify where and when low adhesion is going to occur on the rail network. This will allow targeted action at these specific locations, to help manage the safety risks and reduce delays.
RSSB is collaborating with the University of Sheffield to develop a tool using artificial intelligence to help predict low adhesion track conditions.
Low adhesion track conditions are a serious safety and operational issue for the rail industry, costing around £350million each year. It causes delays affecting train performance and can result in station overruns and signals being passed in danger.
Temperature, humidity and the presence of leaf layers or other contaminants all have an impact on the level of adhesion between the train wheel and the rail. The project will use artificial intelligence to analyse data, and high-resolution video footage to deliver more accurate predictions about friction at the wheel-rail interface.
One of the project outputs will be an online tool, for users to enter data that will generate friction predictions for anywhere on the network, in time for Autumn 2023.