How could AI predict leaves on the line?

Paul Gray, professional lead of engineering and research at RSSB, notes that pattern recognition and AI will provide an estimation of the localised railhead adhesion conditions.

“The railway works hard to manage low adhesion conditions using a set of technologies and processes that in the main can cope with a range of adhesion conditions that can vary throughout the year and typically are most problematic in autumn,” says Gray.

“However, sustained very low adhesion conditions can still occur and can lead to trains not being able to slow down and stop in a controlled way which can cause station platform overruns, signals passed at danger and very occasionally more serious incidents such as the collision that happened near Salisbury in October 2020.”

This project is part of ADHERE, which is the cross-industry adhesion research programme aiming to deliver research to achieve adhesion conditions that are unaffected by and independent of the weather and climate.

RSSB is using its previous research which found that friction conditions on the rail head can be estimated using images of the surroundings, images of the rail head, and other sensor data.

“Having detailed estimations of localised adhesion conditions can help improve operational decision making and railhead treatment planning in the short term and could offer a way of reliably comparing one route to another to help focus further investments to mitigate low adhesion,” explains Gray.

“This capability would help drive down delays to passengers caused by low adhesion.” 

As the feasibility of this project has been approved, work on an extension of the new tool to other infrastructure types is being carried out. There is also a focus on developing a prototype adhesion estimation tool for the initial trial.

Britain’s rail network contains around 20,000 miles of track, and AI technology can assist in the efficient monitoring and management of this network. This can prevent disruption to passenger journeys as well as freight journeys.

Management strategies are usually based on meteorological forecasts as well as local infrastructure condition information. It is expected that information provided by AI technology on this subject will be more accurate.

Gray offers a positive outlook for the future use of AI with regard to the prediction of low adhesion track conditions.

“Low adhesion conditions can occur rapidly and be highly variable in different locations and from route to route,” He says.
 
“Using images that are routinely captured, and then using an AI pattern recognition approach offers a realistic way to estimate low adhesion conditions to help prioritise treatments and target investments to drive down disruption caused by low adhesion.”