Data-driven rail

Source: RTM Oct/Nov 2018

Andy Cross, director of Elastacloud, and Lucy Bealing, from the Elastacloud Rail Centre of Excellence, discuss their innovative collaboration with Porterbrook to deliver data-driven railways.

The march of progress has an unequal tempo across industry bodies. The information technology sector has transformed the way we live and work, yet the rail sector is amongst those who have experienced iterative benefits from progress rather than revolutionary transformative digitisation. It is the requisite complexity that must be overcome that leads to this inequality. IT is free to solve the easiest challenge with technology and move on to next. Rail has inescapable challenges that technology needs to solve to be relevant. Some now believe that this is possible and that the time is right for a fresh wave of software innovation in rail.

This year alone, the industry has experienced several incidents, including the chaos that ensued after the redrawing of timetables in May left tens of thousands of users of Thameslink and Northern rail networks unable to travel, which have resulted in increasing pressure for reform of the entire rail industry. Transport secretary Chris Grayling went as far as to say that the whole rail structure is “bursting at the seams” and is “no longer delivering for passengers,” after 25 years of very little change to the organisation and functionality of the railways.

The challenges of high costs, varying resource performance, and an increasingly technological consumer are all present in rail. However, through applying highly digitised asset management, intelligent predictive maintenance, and business and resource optimisation workloads, the hundreds of challenges can be solved with artificial intelligence (AI) technologies.

These exciting opportunities are at the heart of an exciting collaboration between Porterbrook Leasing Company and Elastacloud. Porterbrook is a major ROSCO with a significant asset base of rolling stock; Elastacloud is a data science consultancy with many blue-chip customers in various sectors. So why did we come together in a partnership and what do we aim to achieve?

It’s an exciting combination of challenges, grounded in a hardware asset fleet that’s difficult to physically innovate with, presenting a software opportunity that is largely untapped. Elastacloud will be looking for the elusive patterns in data that show where things can be improved.

Elastacloud has established a centre of rail excellence with an international team building on their experiences in other sectors to work intimately with Porterbrook and its customers to build and field test AI products that use data at their heart.

Darshna Shah, a data scientist at Elastacloud, explained: “Porterbrook has provided an interesting set of challenges for us to overcome together, which has been exciting for myself and my team. The data we already have to work with is promising, and we believe the potential for real leaps forward within the industry as a whole are possible with the application of advanced data science techniques to the reams of data that can be drawn from sources throughout the industry.”

Working very closely with Porterbrook, Elastacloud will have exceptional access to data and industry knowledge, a potent mix when combined with our industry-spanning experience. The major focuses of the partnership are:

  1. Predictive maintenance: Mary Grant, CEO of Porterbrook, sums this up as “the right intervention at the right time,” such as using data signals to conditionally maintain rolling stock using a dynamic service regime rather than static paper-based maintenance plans;
  2. Asset management: the ability to track and build an intelligent rolling stock and affect intelligent patterns of use that maximise return on an asset;
  3. Resource optimisation: varied fleets doing what is needed of each route, not block purchased.

The partnership will aim to bring the current fleet of rolling stock into the 21st century without the need for expensive upgrades which have traditionally meant increased prices for customers – instead using the data that already exists in an intelligent manner, starting the industry on the long journey towards transformative digitisation.


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