VIEWPOINT
Benoît Leridon is a regional IPR Business Development Manager at Nokia, responsible for Transport, Energy and public sector in EMEA.
AI's Rail Potential Benoît Leridon, Head of Transportation, Network Infrastructure, at Nokia on the latest opportunities in digital transformation
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ailways are rapidly moving towards a digital transformation, using the latest technologies to increase reliability, safety, and sustainability. The use of data analytics is a crucial component of this digital change, supporting quick and efficient decision-making for railway staff and powering automated processes. In particular, recent improvements in AI and machine learning have attracted a lot of interest for their ability to automate many processes and allow natural language communication between digital systems and humans. This combination of technologies has great potential for the railway industry. Much of the media focus has been on the recent advances in AI and machine learning, but these are not the only components that are needed to make the most of AI's potential, starting with the railway’s shift of operational technologies (OT) applications and data to the OT cloud. While devices, and Internet of Things (IoT) sensors and cameras constantly gather data and video from all aspects of rail operations, it is 4G/ 5G wireless access and high-speed IP/MPLS optical backbone networks that ensure the data is transmitted reliably and without lag for processing in the OT cloud reserved for mission-critical rail applications. This ensures that information is analyzed quickly and results are sent back immediately to automated systems and operational staff making safety- and business-critical decisions. For a more data-driven operation, the data centre fabric (i.e., the networking inside a data centre) is becoming especially important in the overall mission critical communications network.
The potential of AI
AI can be used by railways for different purposes to improve operations, achieve efficiency, and boost passenger experience. One example is the use of video data from sources like CCTV, cameras on trains and
drones. AI applications can examine the video to detect irregularities or possible problems in railway infrastructure conditions, such as tracks, rolling stock and level crossings. This capability for early identification can help avoid accidents, ensuring the overall safety of railway operations. By using different types of data, such as data from sensors on trains and tracks, it is possible to anticipate when parts will break down. This enables maintenance to be done before problems occur, which reduces delays and prevents expensive fixes. Also, monitoring train, track and weather conditions in real time can help to manage train timetables, traffic, and the overall operations better, which helps operators to adjust to different situations and leads to higher efficiency and reliability. AI will eventually be used to implement and operate automatic train systems. This involves automated systems for braking, accelerating and controlling the trains that can improve speed, lower energy use, prevent delays and eliminate human mistakes and crashes. AI can also enhance energy management, customer service experience, scheduling and routing, capacity planning and asset management. Moreover, AI will be crucial for ensuring security and safety, both physical and cyber. Applying AI in these various areas will result in a more effective, secure, and dependable railway system, ultimately serving the interests of both the rail operators and passengers.
The OT cloud relies on mission-critical communications
AI and machine learning need a lot of both compute and storage resources to analyse huge amounts of data and video. However, during operations, the most vital piece is the network resources that support them, because they make sure that real-time data can be gathered, processed and acted on fast
and dependably. This is especially important for railways that use remote control and automated applications, as they require a robust and effective data centre fabric. Railways’ data centres are used to mainly deal with IT applications. Usually, they had few OT applications, so data centre network reliability was not considered critical. As railways benefit from and use the OT cloud, data centres have to improve their performance. New developments in networking technology allow railway operators to adopt new data centre technologies with confidence to support mission-critical OT applications for realtime decision making. As OT applications change to use modern software paradigms such as containers, microservices and Kubernetes, these new data centre networking features also let them achieve more speed and agility without compromising reliability and stability.
Optimisation of the overall OT cloud network
The OT cloud network connects smart devices and sensors on board and along the track, through the access networks and the wide area mission-critical IP/MPLS optical backbone, to the data centre fabric. This network has to achieve high performance from end to end. Data center fabrics have not achieved the same level of reliability as backbone networks, which can reach five-9s availability. To improve the DC, operators need to design a strong level of resilience and redundancy, predictable quality of service (QoS) that can be customised for each application, and a solid multi-layer and zero-trust security framework to protect against cyberattacks. This robust DC fabric also needs to connect smoothly with the railway’s mission-critical backbone so that digital assets and equipment on the railway network can run their applications well. The network has to be able to assign whatever resources are necessary to maintain a stable QoS policy across the DC and backbone with very high reliability. At the access network domains, the network has to be capable of fulfilling all these performance requirements and to transport data from digital sensors and smart devices like interlocking and RBC, connecting trackside and onboard systems. If railways want to use advanced AI/ ML OT applications effectively, they need overall OT cloud network connectivity that is optimised, flexible and secure. The network, including the data centre fabric, has to be very robust, secure and reliable, because many automatic processes depend on it for critical control. When rail operators think about how to benefit from the AI revolution, they have to pay attention to the OT network and cloud infrastructure that will be needed to support data-driven operations in real-time. 53