Britain’s railways have never been busier. Network Rail (NR) carries 57% more passengers than it did a decade ago, with travellers expected to make 400 million more journeys on NR’s 20,000 miles of track by 2020. But with its Victorian-era rail infrastructure incapable of supporting the double-decker trains that have been deployed to meet increased demand in other countries – and hampered yet further by complex maintenance processes – it has been critical for NR to find new ways of realising its vision to deliver ‘a better railway for a better Britain’.
Step forward CSC. Having been mandated to improve NR’s understanding of its asset base – including signalling, track and drainage – with a view to increasing utilisation of those assets and making maintenance decision-making easier, CSC’s consulting team wasn’t interested in designing a run-of the-mill solution. Instead, it looked to the cutting-edge defence sector for inspiration.
CSC knew it needed to capitalise on the latest big data platforms and geographic information systems to help NR achieve its objectives of meeting a stringent 92.5% Public Performance Measure (PPM) target by March-2019, while reducing its Fatality and Weighted Injury (FWI) rate by 14.5 points per annum over the same timeframe. Its response was to develop the Rail Infrastructure Network Model (RINM), at the heart of which sits intuitive visualisation software – the Geo-RINM Viewer – that employs pioneering geospatial technology and aerial surveys using the same trailblazing imaging equipment adopted by the US military.
This innovative solution provides fieldworkers with a detailed, desktop visualisation of the entire network’s assets – single-view maps so powerful, they can accurately profile the safety risk of a single tree based on its infrared signature and, calculating the density of tree canopies, can predict where falling leaves may affect rail lines.
The tool, which enables users to locate and access detailed information on specific assets, is highly interactive – giving fieldworkers the ability to feedback on and share maps with colleagues, ensuring they’re constantly up-to-date. Crucially, by integrating asset and survey data, it has also helped NR become the first rail operator in the world with the capability to accurately assess how different assets impact each other.
With the first release of RINM and the Geo-RINM Viewer platform delivered in just over seven months, ahead of schedule, a wider roll-out and subsequent releases commenced in October-2014, and is timetabled for completion by 2017.
The collaboration with CSC put quality asset data at the centre of NR’s decision-making. As a result, NR is now forecasting:
- Savings of £45million by March-2019.
- Spending 2.7million fewer hours on-track, annually.
- Executing projects 7.2% faster through improved data-sharing.
- Spending less time on data gathering and integration, freeing up to 13% of FTEs within the central team to focus on value-adding analytical work.
- Being upheld as an example of best practice, sharing its model both within and outside of the rail industry.