AI tool to tackle ‘leaves on the line’ rail disruption

Artificial intelligence (AI) will help tackle rail disruption from ‘leaves on the line’ as part of a new project.

The AI-enhanced tool, which will help predict low adhesion track conditions, is being developed by the Rail Safety and Standards Board (RSSB) and the University of Sheffield.

The partners’ new research project is investigating how more detailed information on local conditions can be used to tackle the seasonal challenge.

Low adhesion track conditions are a serious safety and operational issue for the rail industry, costing around £350m each year. It not only causes delays affecting train performance, but can also result in station overruns and signals being passed at 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 AI 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, planned for release before leaves fall this autumn.

“While people may think of leaves on the line as a joke, or just an excuse used when a train is delayed, the reality is that it's a very serious issue for the rail industry. Low adhesion causes significant safety risks and operational problems, costing millions of pounds to manage,” said Paul Gray, professional lead for engineering at the RSSB.

“Our new research project will use AI 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.”

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