As Europe wrestles with the challenge of climate change, there has been an increased focus on encouraging the use of trains instead of planes. Indeed, in the UK, railway usage is already well on the way to a post-pandemic recovery and expansion with passenger numbers reaching the milestone of 90% of pre-Covid levels on Thursday, May 19, and then reaching 92% for the following three days.
Statistics show that passenger numbers are returning both during weekdays and weekends, approaching the highest levels since March 15, 2020.
On course with current projections, passenger and freight travel are expected to double by 2050, amplifying the importance of optimising railway safety and security.
As a greater number of travellers look set to rely on trains for both business and pleasure, now is the time for railway organisations to embrace digital transformation.
Apt transformation of infrastructure planning and operations is the solution for slashing maintenance costs, improving service quality and reliability, maximising the use of assets, improving environmental sustainability and increasing revenue.
Undergoing this process will help rail operators to meet the predicted growth in demand of their services as people increasingly look to travel more sustainably.
Key to increasing the reliability and optimisation of UK railway networks is the implementation of emerging, data-driven technologies, such as digital twins and the use of artificial intelligence (AI), for smart monitoring of railways.
AI and LiDAR in infrastructural mapping to improve rail networks
When it comes to improving the infrastructure of railway networks, efficiency and safety go hand in hand. After all, an inefficient network increases safety risks, while safety issues cause disruptions and delays. Here, AI and laser scanning technologies are vitally transforming the precision of railway infrastructure monitoring for improved efficiency and safety.
In the UK, a collaboration between Network Rail and Innovate UK is an excellent example of how these technologies can be used together. This new approach makes use of 3D laser scanning in combination with AI to automate the collection and analysis of railway data.
Mapping railways is vital to ensure there are no objects on or near the tracks that a train might collide with. Rail operators must guarantee safe clearance between the trains and the edges of nearby objects. Network Rail continuously collects data from its railway tracks and the surrounding landscape to form an accurate picture of spaces between trains and their immediate infrastructure.
Traditionally, the data was collected and analysed manually, a highly time-consuming task that could take months, even years.
Instead, using mobile laser scanning tech, railways can collect 3D data, creating a 3D model of the network more easily.
Once the data has been collected by the LiDAR technology, it is then automatically analysed by AI to identify different structure types and perform measurements on nearby structures to the railway network.
Previously, this was a manual activity that required specialised expertise, but now AI models can automatically process unstructured data and accurately output target objects, for example nearby trees or infrastructure, for further analysis.
While saving time and money, AI can rapidly process huge pools of data across the railway network, flagging any potential problems to operator teams. Not only does this allow for the faster identification of any potential clearance issues, but it allows the human teams to dedicate their attention to where it is needed most, thereby improving safety overall.
The implementation of these technologies lowers railway risk, ensuring trains can travel without accident or hindrance.
This smooth running of UK railways will be essential in meeting the increased demand placed on railway infrastructure, as the country progresses towards a more sustainable future.
Using digital twins to improve rail efficiency
Railway infrastructural efficiency can also be greatly improved by using digital twin technology, which merges the already captured 3D model of the network and its adjacent infrastructure with other planning and operational data, including real-time data (such as positionings).
This creates an identical replica of the whole network and its features, including tracks, bridges and even specific details such as benches, rubbish bins and trees, linked with business data and processes, from works projects to service status.
This digital version of the entire railway operation enables insight to the specific areas of immediate issue in the network, simulates different scenarios, as well as helping to predict any future risk events, using AI-powered automated analysis.
The combination of the digital twin with AI means the system can flag fluid variables that could have an impact on rail network safety, such as high congestion, providing operators with the chance to alleviate potential issues before they result in risk and disruption.
Indeed, the Rail Safety and Standards Board (RSSB) is developing a National Digital Twin (NDT) programme for the
This would be an ecosystem of digital twins joined within a standardised protocol through which information could be shared securely to benefit the economy and the environment.
Digital transformation underlies a more sustainable future
With railway traffic expected to increase exponentially as travellers seek more sustainable methods of transportation, implementing emerging technologies will be fundamental to ensuring high-quality and robust service across the UK’s railway networks.
More intelligent railway monitoring, digital twins and the implantation of AI-powered data solutions will increase the resilience of Britain’s networks to facilitate this change.
Safety is of paramount importance, as unsafe railways are prone to accident and delay, which, in turn, reduce confidence in the national railway network and potentially push travellers toward less sustainable methods of transport.
The increase in railway usage will, accordingly, escalate stressors on the network.
Therefore, it’s essential that innovation is put to the task of autonomously managing the data that lies behind the smooth operation of the nation’s railways.
A green future is one in which railway networks operate at great efficiency, no matter the demands placed upon the infrastructure.
By providing this optimisation, network operators can become an integral part to ensuring railway travel becomes an intrinsic part of the UK’s greener future.
Dr Uwe Jasnoch is the director of government and transportation for the EMEA region at Hexagon’s Safety, Infrastructure & Geospatial Division. He is a frequent speaker and author on topics related to public sector and transportation technologies, sustainability and resilience. Before joining digital reality solutions company Hexagon in 2007, Jasnoch founded the company GIStec, a spin-off from Fraunhofer-IGD, where he oversaw the GIS department.