19.03.18
Winners of £500,000 Data Sandbox competition revealed by RSSB
The Rail Safety and Standards Board (RSSB) has revealed the winners of its £500,000 competition, aimed to help develop novel ideas and solutions to key network performance challenges.
The ‘Data sandbox: Improving network performance competition’ was launched in October 2017 via the Rail Research UK Association (RRUKA).
It will fund six feasibility studies to look at how industry data can be used and analysed to reduce delays, improve dwell times, better understand train movements, and improve overall network operational performance by developing tools to support and optimise performance management.
A feasibility study on developing an intelligence ensemble system for predicting and preventing train delays was one of the winners, and will be led by the University of East Anglia with support from Network Rail and Greater Anglia.
Liverpool John Moores University will lead a feasibility study on anticipating and mitigating reactionary delays with support from Mersey Rail.
The third winning entry was for an agent based modelling and visualisation of the causes and consequences of knock-on delays, led by the University of London and supported by Risk Solutions and Great Western Railway, will aim to plan different strategies to for complex delays and compare their effectiveness.
The University of Southampton will lead a study into predicting and mitigating small fluctuations in station dwell times, with the aim of developing models capable of making real time predictions, and will be supported by South Western Railway.
A feasibility study looking at providing data analysis insights into real-to-the-second timing patterns of passenger rail services using machine learning techniques, led by Middlesex University with support from Southeastern Railway, has also won funding and aims to provide more accurate models and estimates about station dwell times and between station track section travel times.
The final project to win funding was Leeds University, with support from Network Rail and First Rail Holdings, to look at fusing train movements data with weather data in order to better understand and model train movements and driving behaviour under different driving conditions.
Top image: Victor Huang
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