The UK Rail Safety and Standards Board (RSSB) and the University of Sheffield are collaborating to investigate how more detailed information on temperature, humidity and the presence of leaf layers or other contaminants can be used to predict low-adhesion track conditions caused by leaves on the railway line. The project aims to combine high-resolution video footage with artificial intelligence (AI) data analysis to deliver more accurate predictions about friction at the wheel-rail interface. One of the outputs will be an online tool to generate friction predictions for anywhere on the network in time for autumn 2023. The project forms part of the cross-industry ADHERE programme of research to achieve adhesion conditions that are unaffected by the weather and climate. This builds on previous research by RSSB which found that rail head friction conditions can be estimated using images of the rail head and surroundings and other sensor data.
