Systems, engineering and technology consultancy, Frazer-Nash, in collaboration with the University of Hull’s Logistics Institute, is to undertake a project for rail research body, RSSB, to explore how using data can improve performance and short term planning on the rail freight network.
The project, Rapid Evaluation and Planning Analysis Infrastructure for Railways (REPAIR), is one of ten awarded funding under a grant of £4 million by RSSB in collaboration with Network Rail. It will examine how data analytics and machine learning techniques can offer new ways of predicting and mitigating the propagation of delays on the network.
Chris G Jones, at Frazer-Nash’s Middlesbrough office, will be project managing this work. He describes what will be involved:
“Funded by RSSB under its Data Sandbox+ competition, we’ll be bringing together Frazer-Nash’s skills and expertise in Machine Learning with the University of Hull’s powerful NR+ suite, to develop a predictive engine for propagation of delays on the rail network.
“Historically, route setting and freight planning has been carried out by a combination of expert knowledge and laborious paper systems. The development of the University of Hull’s data tool, NR+, has significantly improved this process – as a visual rail capability mapping system, it provides a powerful toolset for interrogating and searching the infrastructure constraints of the network.
“The REPAIR project will further build upon NR+, to explore how data and machine learning can inform freight operators’ very short term planning (VSTP) in response to incidents on their networks. It will develop a tool to enable freight operators and Network Rail to better predict the effects of the VSTP decisions they make, supplementing the functionality within NR+ to allow them to consider both current delays on the network, and future predicted delays.
“These predictions will then be embedded into NR+, and will provide an additional level of decision support for signallers and control staff when evaluating the optimum response to run a VSTP request. Once developed, the machine learning toolsets will be able to carry out ‘So What If’ testing for freight planners, signallers and controllers, enabling them to see the potential impacts upon their route from delays across the network; and ultimately to help them choose the most robust route and time for delivery and to minimise disruption.
“We’ve also formed an advisory board, made up of major players in the rail freight operating sector, to help steer this work and to ensure it meets users’ needs.”
Amar Ramudhin, Director of the Logistics Institute, said:
“We’re delighted to be working with Frazer-Nash to further enhance our pioneering NR+ platform. Our combined expertise will deliver major efficiency gains to the rail planning industry, not only in the time needed to find the best routes, but also in faster decision making. This is essential for VSTP.”