Data Sandbox: Improving Network Performance
In October 2017, RSSB launched the £500,000 Data Sandbox competition to identify novel, data-driven solutions to key network performance challenges. We awarded funding to five projects.
The academic-led feasibility studies successfully integrated existing datasets from a wide range of sources to provide new insights. Together, they showed that new approaches to data – such as machine learning and graph theory – can play an important role in helping the industry drive performance improvements.
Three projects focused on understanding the impact of reactionary delays and developing techniques that could be used to identify mitigations.
- City University of London and Risk Solutions – with support from Great Western Railway – developed a method for investigating the causes, severity and likelihood of reactionary delay under different scenarios and understanding delay dependency between locations.
- The University of East Anglia, with support from Greater Anglia, developed software to help train controllers determine the knock-on effect of primary delays
- Liverpool John Moores University, with support from Merseyrail, developed an optimisation model to determine the extent to which tactical interventions could limit the impact of reactionary delay.
Two projects explored how machine learning and data visualisation techniques can be applied to help reduce dwell time variability.
- The University of Southampton, with support from South Western Railway, developed high-resolution maps of dwell time hotspots that can be targeted for mitigation measures.
- University of Middlesex, with support from Southeastern, identified correlations and recurring patterns to predict real to-the-second timing patterns of passenger services.
Building on the success of the original competition, Data Sandbox+ was launched in 2019.