26.01.18
The data-driven railway
Martin Vallance, Network Rail’s decision support tool (DST) workstream lead, reveals how a new digital track tool is set to reduce temporary speed restrictions (TSRs) across Britain’s railway.
Imposing a TSR on the rail network has far-reaching and negative impacts on train performance. Aside from the obvious delays to passengers and freight, it results in penalty costs to Network Rail, reputational damage and hinders our ability to run an accurate timetable.
Passenger numbers have doubled in the last 20 years, and this unprecedented growth is set to continue. Around 22,500 trains run every day in Britain – with nearly 1.7 billion passenger journeys each year – and another 400 million rail journeys will be made by 2020. This success comes at a cost to an infrastructure that is taking an increased hammering every day. To meet this demand we had to find a new and radical approach to meet the challenge.
Digital transformation
In response, Network Rail launched the Offering Rail Better Information Services (ORBIS) programme in 2012. ORBIS – a key part of the Digital Railway Programme – was set out principally as an infrastructure knowledge service that collects, evaluates, collates, analyses and communicates intelligent data to the business by placing quality asset data at the heart of decision-making in Network Rail. Quite simply, we had to find ways of getting more out of the existing – predominantly Victorian – infrastructure.
Over the past five years, ORBIS has introduced apps and tools specifically designed to capture and exploit high-quality asset data and new ways of viewing the railway.
One of the programme’s key projects is the design and delivery of digital DSTs across signalling, track, electrical power, operational property and switches and crossings (S&Cs). Via dashboards, this suite of DSTs is designed to give engineers access to up-to-date, aligned asset data to help them make more informed decisions on the optimal time to carry out maintenance or renewal work.
Up until now, accessing this type of data meant manually digging through data streams held in different formats, with different IDs and location references. What could take days of work would now be reduced to minutes with the DSTs.
Right work, right time, right place
Our colleagues in the routes quickly saw the benefits. Teams focused on track performance then challenged us to design a DST to include data on the rail asset to help them predict when and where a TSR was likely to take place.
In response, we upgraded the S&C DST with TSR track data to produce the Track DST. The need for this capability was clear: in the last period alone, there were 191 unplanned TSRs costing Network Rail £2.9m.
To achieve this, a small but dedicated team was tasked with collating key track data from 17 different source systems. This data had to be cleaned, aligned and in one place. It needed to show tangible data engineers could work with to carry out the right work, in the right place, at the right time – ultimately, that’s what the Track DST was designed to do when we launched the prototype in 2017.
Frontline solution and accurate predictions
Consultation with our core users, track maintenance engineers (TMEs), was critical to the tool’s success. Despite the complexities of the track asset, the Track DST treats track as a whole system by bringing all the different asset types together. The Track DST is split into two sections: first, workbank planning and low-level track data; second, TSR management and predictions. With clear 220-yard section breakdowns of the track asset, it identifies track with poor condition and performance and provides evidence to see trends through a range of clearly laid out data columns, including track quality, degradation rates, track geometry faults and S&C condition failures.
The Track DST gives a level of insight not seen before on the railway – in fact, nothing like this exists anywhere else in the industry. It takes the data we already have from data collection trains, track surveys and maintenance regimes (using iPads to capture information) and applies simple analysis and algorithms to give powerful insight to engineers. It’s important to stress that the Track DST is not replacing engineering expertise, it’s merely enhancing and supporting that knowledge.
Track geometry
Track geometry faults are a significant cause of TSRs, and Network Rail has a five-part grading system to measure their severity: good, satisfactory, poor, very poor and super red. Super reds will, in the majority of cases, result in a TSR and in the worst-case scenario to derailments.
The DST’s dashboard gives a clear indication of the rate of change to track geometry identified by the data collection train each time it passes a section of track. From this, a degradation rate can be established which provides a specific future date – clearly listed within the DST – showing when a segment of track is going to hit a super red category. This date can be compared to the scheduled inspection or renewal date column, and if the super red geometry date is earlier, the decision can be made to intervene on the asset in time to fix a fault and eliminate the need for a TSR.
Once this has been established, TMEs can view the column showing the maintenance history carried out on that segment of track and, from this, determine if the work has been effective or not. It gives TMEs the evidence to know what work they should carry out, from tamping and stoneblowing to lifting and packing, and to see what interventions have worked and take the right future actions to rectify the fault. From this we can start treating the cause of the fault, not the symptom, and move to predictive maintenance regimes.
Putting the data to the test
Before a TME could use the Track DST confidently, they needed proof it could deliver. Using a proof of concept version we worked with a team in the LNE route – one of Britain’s most congested routes – to see if the DST would have identified segments of poor track geometry from the previous year that led to TSRs being imposed. The results were overwhelmingly positive. The super red prediction capability within the DST accurately predicted the location of 70% of all TSRs on the LNE network.
During trials, a repeat TSR showed maintenance teams visiting a site four times in three years to ‘dig wet beds’ in an effort to rectify a fault. When we analysed the earthwork and drainage data in the DST, it clearly demonstrated an underlying earthwork/drainage issue. This evidence was proof that the repetitive work was not fixing the root problem – it required off-track teams to carry out work on nearby drainage and earthwork assets to rectify.
Data today – better tomorrow
As we roll out the live tool across the routes, teams are already talking about the possibility of using the DST’s data to establish automated tamping regimes or carrying out cost analysis of each TSR to see the exact cost-impact that reducing a speed from 90 to 60, or lower, will have. This will help route asset managers compare the cost of fixing a fault to the cost of running trains at reduced speed and carry out the best course of action. Ultimately it will mean track teams can identify the work that will have the most impact, and by eliminating the need for time-consuming trawls through multiple data sources to get the information they need, engineers can focus their time on engineering decisions to keep train services running safely and efficiently.
Reducing TSRs will play a critical part in achieving our ambition to increase both capacity and frequency of trains on the network. We don’t have any choice. By exploiting the data we have through simple digital tools like the Track DST, we can make that shift to new ways of working that will help us deliver a railway fit for the 21st century.
Top image: ultraforma