A sustainable rail infrastructure requires new data solutions

Aerial view of the derailment. Image: UK Government

Geoff McGrath: CKDelta has supported multiple rail companies for both planning to meet expected demand as well as supporting real-time decision-making during operations. Current engagements cover planning and operations for national rail companies in Denmark, Austria, Italy, and Singapore.

This insight is driven by a comprehensive data set derived from multiple industries including telecommunications, retail, and utilities. This data is applied through predictive analytics, which can predict consumer behaviour in real-time, meaning the rail industry is able to react to changes in demand. CKDelta is processing 25 billion data records every day – allowing for the generation of insight and intelligence at an industrial scale.

The principal approach the industry should adopt should be predictive analytics and simulation for design and decision support for both planning and operations. This will increase efficiency and can be used as real-time simulation in the decision cycle of operations as unexpected situations unfold. Predictive analytics can be used to identify patterns found in large data sets to identify future risks and opportunities.

Predictive, real-time passenger footfall data can be used to track the evolution of demand which helps determine the priority areas of renewal and maintenance of the railway network. By understanding passenger traffic it is also possible to plan maintenance work. Passenger footfall data can also help plan maintenance work at a time where disruption is minimised.

Predictive analytics can be combined with machine learning to create ‘digital twins’ which can help create a near-complete model of future scenarios. By continuously comparing these models to new data insights our algorithms will evolve to become more accurate over time and also more adaptive to future shocks.

By combining data from multiple sources like demographics, interests and movement pattern, a holistic picture of population movement, purpose of trip, origin and destination and even mode of transport can be built up. This will help to understand and predict population behaviour and even cause greater adoption of rail transport.

Securing the future of rail travel means utilising minimum data for maximum insight. Predictive analytics is not the silver bullet but it is far more capable at tackling the industry challenges than outdated historical models.

With the use of predictive analytics and modelling, insight can be provided at a system wide level in real time which means decision makers have the ability to be more reactive and take decisions that generate maximum value. Diverse data sets provide a greater opportunity for human-centric design to offer services that fulfil current and future demand. Changing behaviour from individuals driving to their destination to maximising use of mass transit systems and public transport is key to meet emissions targets in the future.

Modelling means that developers can test possible approaches earlier in the development process to determine the optimal solution.

In certain countries there is a high bar for rail developers when it comes to providing supporting evidence during the planning application process. This means transport assessments are often months out of date by the time applications are considered. By providing a more current view of travel patterns, data insights give developers up to date knowledge in order to back up their proposals with confidence.

The challenge of deploying sustainable rail infrastructure in a timely fashion already requires significant investment and manpower. The volatility of the world economy following the pandemic means that budgets and workforces have been stretched further – which has consequently made this transition harder.

The combination of supply chain issues and high global energy prices have made essential materials increase in price whilst simultaneously becoming more scarce. This means that the maintenance and renewal of railway stations, lines and trains cost more and take longer.

To compound these issues the changing climate has brought more instances of extreme weather which is unpredictable and places further pressure on infrastructure. In order for the network to sufficiently react to this unpredictability, real-time strategy and decision support tools are needed which require quality data feeds. Such systems do exist in industries such as advanced manufacturing and the extractive industries and even F1 motor racing. To date, they are not widely deployed in transport operations.

As a result of the economic environment and external factors the initial goal of widescale deployment of sustainable infrastructure is threatened with being made desirable rather than essential. If this is to be prevented the rail industry needs to look to innovative solutions, such as the utilisation of data analytics, to maximise the value of resources.

The need cases for major rail projects are built on pre-pandemic data points and assumptions that do not match the circumstances we are in. Project sponsors should embrace insights from new data which could actually help bolster business cases and, even more importantly, make the final product more likely to meet society’s future needs.

Modelling scenarios in real time will make delivering sustainable development goals such as net zero possible. The ability to share data between public and private sector bodies also means we can more closely interrelate different sectors in our planning cycles and provide greater flexibility against evolving long and short-term trends. Now is the time to reset our baseline assumptions and deliver smart and sustainable planning for our communities, and their transport requirements.

Image of Axel Schuppe. Credit: The German Railway Industry Association.