How do we use the power of data to transform rail?

For example, transport operators such as LNER are introducing new innovative ticketing approaches from analysing travel patterns and customer preferences; infrastructure owners, including Network Rail, are using machine learning techniques to help detect defects across multiple types of assets; and new intelligent command control and signalling technology is being rolled out worldwide at scale. Your organisation may well be involved in these or similar initiatives, which are all in pursuit of the vision of a punctual, safer, more reliable, passenger centric and cost-effective railway service.

At the same time, the industry faces the key challenges of ageing assets, cost efficiency challenges and the need to adapt to changes in weather patterns arising from climate change. Much of the industry’s data remains out of reach of machine learning and AI algorithms, being stranded in disconnected silos.

The question is: how do organisations in the rail industry “double down” on the discipline of data and digital so that it can do even more to support and in fact drive the vision of an improved railway, and enable transformational change to address key challenges?

We believe that there are four key questions to ask your organisation, to unlock this potential:

1. “Have you a vision for data?” This should be aligned to organisational goals and sets the direction for downstream initiatives. Most importantly - be clear about what you want to achieve with data and digital. Too often, we see digital projects with a stated aim to “deliver x new system” or even to “implement artificial intelligence”. Such projects often lose their connection to the needs of users or funders, increasing the risk of project failure. Instead, consider outcome focused business cases such as “we want to improve train performance” or “we want to give real-time decision makers more trusted information”. And as you progress through delivery, continue to challenge yourself over whether your decisions are still in the best interests of achieving these business cases.
2. “Have you appointed data leaders across your organisation, with accountability both for how data is managed as a strategic asset, and for delivering to support this strategic vision?”. This is important in order to persuade strategic and operational decision makers of the benefits of a data and digital approach, and to create alignment of activity at multiple levels in the organisation.
3. “Do you have a programme of data delivery “. This should start with getting the basics right, especially understanding the what, where and the purpose of your existing data. Understand how data informs your business outcome (a decision, an intervention, an ongoing process), then trace that data back through the lifecycle, understanding who owns it, where and how it is managed. Consider the quality of that data (both real and perceived) and identify where improved quality and structure of data will offer more value to improve business outcomes. Before you can do anything clever with data, you first must understand the data you have and be capable of managing it appropriately.
4. Lastly, “does everybody, from your execs to your engineers understand the value of data in today’s railway, in meaningful ways?” . Consider education to improve skills and knowledge of data and digital practitioners including data engineers, code developers and product owners, in order to deliver digital outcomes of good quality. Education to improve skills of digital and data product clients and buyers, so that the right digital outcome is procured in the first place. Education of senior leaders to support growth of the right culture, and to enable resources (including funding and political backing) to be given to promising initiatives.

Within the rail industry, one of our clients was looking to assess the benefits of digital twins and feasibility to their operation, to sort the reality from the hype. This started from the question of “how do I buy a digital twin?”.

To address this question, Arup took a step back to define a wide-ranging model of that area of the business - in this case, the operational running of a rail network. Through this business-focussed model, we were able show where different elements of business activity interface to deliver both the daily train service and the infrastructure the services run on. These links are currently a collection of legacy systems, information exchanges by email or spreadsheet and/or regularly human interfaces by phone calls and meetings.

We demonstrated how a digital twin approach could bring these previously disparate data sources into a unified common model of the current and planned state of the network, enabling automation of interfaces and data exchange between business areas, clearer visualisation of information and freeing up business time for decision making and problem solving. The structured model also enabled digital components to be procured and built in a modular and incremental fashion, reducing overall delivery risk and the need to go for one big-bang investment.

Looking beyond the question of digital twin, our team are supporting Network Rail to define the business case and roadmap to demonstrate how improved data literacy in business team could achieve a step-change in delivery of railway operations, as part of their ‘Better Data for Better Operations’ programme.

Every day on the railway, thousands of operational staff analyse multiple data sources to understand the current state of the network, the trains running upon it, and ongoing events. They are then in charge of making decisions about how to achieve the best outcome for rail users that day (passengers making it home on time; freight and goods being delivered). Data techniques and digital products have the potential to automate routine analysis of data, improving modelling of the network and timetable and support better decisions to enable optimised railway outcomes. Network Rail are looking to grow the right capability and put the right support (access to good data; suitable tools; data-centric culture) in place to enable digital solutions to be developed to feed a continuous improvement approach over time.

In addition to our client work, Arup faces similar opportunities in managing our own business. Our internal digital transformation has invested significant resource into education of digital leaders and practitioners across the business, increasing overall digital literacy and giving confidence to try new ways of working. Our internal data (on projects, on engineering design, and about people - our greatest resource) benefits from common formats fed into a common analysis platform, giving an open view of trusted information across the business. And we continue to push the boundary in developing business-focussed digital and data products to deliver faster, smarter, higher quality outcomes to our clients. The outcome has been a step change in the variety of services we offer to clients (including data-driven focus on improving sustainability outcomes – a key strategic aim) and a simplification of internal processes through automation.

Digital transformation is the enabler for a better railway.

Data-driven and digital approaches are critical to enabling improved outcomes in Rail, for example:

• digital twins can deliver improve alignment between track and train, by enhancing real-time insight of the condition of the track, and the operational status of the rolling stock running on it, improving customer focussed management of scarce resources;
• data science techniques (including artificial intelligence and machine learning) can support discovery of new insights to enable better decision-making, especially in management of assets; for example by predicting likely future performance based on models collected from thousands of similar assets in operation, combined with rich data about environment and context (e.g. weather); and
• improved command, control and signalling technologies, supported by data-driven design, configuration and operations offer capacity and performance benefits, whilst reducing line side risk e.g. by reducing ‘boots on ballast’.

These digital technologies need to be underpinned with the right foundations. Organisations should define a clear vision of what they want to achieve; put the right accountable leadership in place; focus effort in developing the right foundations; and invest in data literacy across practitioners and leaders to unlock value for passengers, operators and funders.