The digital response to rail infrastructure hit by climate change

In the UK, recent record high temperatures exceeding 40°C meant major disruption, particularly to travel networks. It was completely unprecedented. Train operators advised passengers not to travel in the extreme heat which caused rails to buckle and overhead wire systems to fail.

Although most people felt that the high temperatures were a unique occurrence that can be disregarded now that temperatures have returned to normal, we should expect a repeat. Met Office scientists predict that temperatures could exceed 40°C every three years by the end of the century if we don’t reduce our emissions. With that in mind, it is vital that the transportation industry, and other industries with a large number of buildings and physical assets, seek to tackle both the symptoms and cause of climate change.

It is a two-pronged attack, where businesses improve the way they respond to extreme weather whilst also making their buildings more efficient to reduce the risk of these events happening in the first place. For train operators looking to minimise disruptions caused by high temperatures or even floods, the answer could be in the way they use data.

Insight from digital twins

Digital twins can help provide the insights that inform how the rail infrastructure responds in extreme weather conditions. Currently, rail operators have to indiscriminately roll out blanket speed restrictions. That is because there isn’t enough data available to see regional variations with enough certainty. As a result, with public safety at play, it is best to err on the side of caution and implement go-slow zones for large swathes of the country. The resulting travel disruptions are clear to see.

With digital twins, operators are able to create an infinite number of ‘what-if’ scenarios and model them on a virtual replica of the network without making any changes to the real thing. The assets we’re talking about monitoring are the vital parts of the infrastructure such as the tracks and overhead wires. If you have data readers on these assets and you’re able to monitor them for a period of time, say five years, you will have a good bank of historical data. This data could be used alongside live data to create operating forecasts. When paired with weather forecasts, you can start to create simulations, based on historical data, of how tracks perform at various temperatures.

Backed with the data, and the tools that come as part of a digital twin, operators can provide accurate speed guidance for trains to run that continue to prioritise safety and also keep trains running as efficiently as possible. It’s business decisions made by real insights.

Reducing environmental impact

The same sort of reliable results can be seen with buildings. If the goal is to reduce energy costs and carbon emissions, with the right intelligence you can hit these objectives without effecting the functionality of the building itself. Most organisations want to cut their energy costs, but without the access to adequate information, they don’t know the right steps to take in making the most savings. Digital twins are useful here because they can help building managers to understand their current usage. They can also build simulations that show how they can have the biggest impact with their efficiency drive.  

For example, Atkins recently worked with Reading train station to improve its energy performance by around 20 per cent. Existing historical data and modelling was used to see the performance of each asset. In cooperation with Cardiff University’s Computational Urban Sustainability Platform, a digital twin was created that could run various scenarios based on simulation data. The biggest consumers of energy were found to be heavy machinery (such as lifts and escalators), and lighting. Both categories combined contributed to 80 per cent of total energy use. Thanks to the digital twin, options could be modelled to see, for example, if escalators were slowed down when not in use, could energy savings be made – and how much.  

The next stage is to install live sensors on the assets so the performance can be understood in real time. By creating a baseline, building management can be alerted when energy usage strays too far away from the agreed threshold. In the future, Artificial Intelligence (AI) could be used to make tweaks automatically so that the building is always operating at an optimum level.

It all starts with data

All of this starts with a data strategy. Building and infrastructure owners need to know how much data they currently have and how much they will collect in the future. As data is the key building block to creating a digital twin, it has to be taken seriously from the outset. However, the challenge is not always technical. It could also be cultural because many organisations are reticent to changing their existing approaches to efficiency. Often, organisations rely on long-tenured building managers who run all of the building’s assets using their experience. But if that person were to leave, all those processes would leave with them. Using data, rather than intuition, is a safer bet in these circumstances, and it helps to maximise energy savings.

The clock isn’t turning backwards. We have to accept that the climate of our planet is increasing and as such, the way we operate our buildings and infrastructure networks has to change. However, we can get more intelligent with the way we respond to immediate challenges, such as extreme heat or flooding, and we can improve the way we prepare for the future by making our buildings more energy efficient. Digital twins are an open-ended solution in that they can continue to produce benefits as organisations grow and their needs evolve.