Rescuing rail with data-driven insight

Geoff McGrath, managing director of CKDelta, makes the case for using real-time data to manage the challenges facing the rail sector.

Geoff McGrath
14 July 2022

Twitter FacebookLinkedIn

Print

Topics 

Digital transformation

Digital railway

Lower passenger numbers, extensive maintenance costs, and the threat of strikes pose a challenge to the long-term future of the UK rail sector. 

Public sector bodies such as Great British Railways, the new government agency set up to oversee the management and future delivery of rail infrastructure in Britain, need the right tools to face these challenges head on, if they are to deliver their upgrade programme and avoid a modal shift to alternative and less sustainable forms of transport. 

Their success will be determined by their ability to ensure resources are allocated efficiently, responsibly, and at the pace required to meet the challenge, while keeping customers front of mind. 

The rail sector must look to adopt innovative data analysis tools that build resilience against future macroeconomic and industry shock such as the pandemic and strike action. The industry has at its disposal a means of providing up-to-the-minute views of current travel patterns through technological partners – including modes of transport, geographical data and time – which enables developers the opportunity to back up their proposals with confidence and sense check them when challenged. 

Building resilience through predictive forecasting 

The principal data tool the industry should deploy is predictive analytics. Simply put, its main functional application is to identify patterns found in large data sets to identify future risks and opportunities. One of its main benefits is that it can help develop data systems that are able to embed intelligence which can be shared openly and at pace across the value chain. 

Real-time and predictive passenger footfall data can track the evolution of demand, a crucial determinant when deciding the priority areas of renewal and maintenance of the railway network. Data-driven insights from predictive analytics models, for example, can be used to determine modal shifts in patterns of travel, helping asset owners more accurately plan for maintenance work to avoid disruption for customers. 

Disruption equals dissatisfied customers, more complaints and the settlement of claims for late train fares. For an industry that needs to cut costs, any unnecessary expenditure is something to be avoided.  

Realising the benefits of early integration

By unleashing the power of predictive analytics, we can also begin to understand the impact any future interventions might have on planning outcomes from pre-application through to application submission. Simulations run within the decision cycle-time would allow both planners and operators to take optimal decisions considering a number of future scenarios in near-real time. As this technology is incorporated earlier in an asset’s lifecycle, the combined potential of automated decision-making with human-in-the-loop strategic input will yield more efficiency and cost benefits in the sector both in the short and long-term.  

Another benefit of predictive analytics combined with machine learning for example, is that the accuracy of a model can be enhanced by inputting more complex variables to understand behavioural changes. 

Digital twins for example, can help create a near-complete model of future scenarios across an asset’s entire lifecycle. By continuously comparing digital simulation models to insights derived from high-frequency data, our algorithms will not only evolve to become more accurate over time, but also more adaptive to future shocks. We can simulate the behaviour of the train network at scale, reducing the need for physical inspections and upgrades. In effect, the more these models are developed, the more we can upscale digital twins for the whole industry. 

A path to future growth

Integrating the use of real time and predictive decision support tool into planning and operations will help maximise cost-effectiveness, reduce expenditure and improve overall customer satisfaction. Incorporating this approach now can ensure that we are able to train data systems with complex variables earlier in an asset’s lifecycle to better account for future shocks that in our interconnected world are becoming increasingly inevitable. 

Only by adopting this technology now can the industry secure its long-term recovery and incentivise travellers to make the sustainable transition away from private vehicles.

Geoff McGrath, managing director of CKDelta.