The future of forecasting adhesion conditions
Scientific developments and rapid technological change suggest that radical new ways of forecasting adhesion conditions could soon emerge. This could lead to improvements in terms of both the accuracy of forecasts and the degree of foresight they provide.
We have made up to £300,000 available to support the development of novel ideas and innovative solutions as part of a longer-term vision for the development of an industry pathway to high value adhesion forecasting capable of providing the right information at the right time to the right people.
The successful projects (outlined below) are expected to publish their findings early in 2020.
Setting the Verification Standard for Adhesion Forecasting – Case Studies: Stagecoach Supertram and Arriva Rail North
University of Sheffield and the Met Office
This project will focus on improving the prediction of low adhesion on the railways in autumn to help mitigate against delays to trains and incidents by developing verification standards at different spatial and time resolutions. This is expected to support those who rely on adhesion forecasts such as train drivers, timetable planners and track cleaning teams. Better monitoring of standards of forecasting is expected to help improve confidence in forecasts in the future.
Feasibility of integrating operational data with adhesion forecasts
University of Huddersfield Institute of Railway Research and the Met Office, in association with South Western Railway
This project will investigate how operational data that is already collected by the railway can be used to enhance knowledge of adhesion conditions and improve adhesion forecasting. This involves validating existing low adhesion forecasts; enhancing forecasts with additional data sources to provide close to real-time information about conditions; and, providing a source of information that could be used to improve real-time operational and safety decision making in the future.
ANTI-SLIP: A study on using Network Rail's and train borne information to anticipate and mitigate the impact of slippery rail
Liverpool John Moores University, in association with Network Rail and Merseyrail
This project aims to construct a framework of integrating and analysing multi-source real-time data – from track-side data streams and train-based datasets - to deepen our understanding of low adhesion and provide better decision support for mitigating its effect. The involves developing higher resolution automated warnings for drivers, and techniques to help validate the effectiveness of current mitigation strategies and suggest optimal solutions.
Networking at the launch of the Forecasting Adhesion competition in June 2018
- Forecasting adhesion
- Big data
- High-value adhesion forecasting