Emerging technologies are reshaping the rail transport sector
Emerging technologies are reshaping the rail transport sector. According to a new study from Nextcontinent, innovations are rapidly improving the safety and experience of passengers – but also boosting internal operations, from analytics helping determine how busy a route will be, to censors helping to pre-empt when and where maintenance is needed.
Born out of the first major industrial revolution, rail transport has survived the test of time, and even in the age of self-driving cars and high-speed planes, trains are still one of the key modes of transporting people and cargo around the world. This endurance reflects rail’s ability to constantly adapt to new cutting-edge technology – and amid a global rush for digitalisation, automation, new communications technologies and standards, it is little surprise that rail systems are once again leading the way in the adoption of everything from modern materials and production engineering to the use of machine learning and artificial intelligence.
Illustrating this, a new white paper by Brockman & Büchner, Curzon Consulting, Eurogroup Consulting and YCP – four members of Nextcontinent, a global alliance of independent consulting firms – has explored how new technologies including AI, the Internet of Things (IoT), sensor technology, and big data are pivotal in reshaping railway operations and enhancing passenger experiences.
Improved passenger experience
Most importantly, technological transformation will improve the experience of passengers. According to Nextcontinent, there are numerous ways in which this is already manifesting itself across global rail networks. Broadly, the researchers note that rail players are gradually shifting and reshaping traditional railway stations into “smart stations.” This includes AI-driven enhancements, which are being deployed to make stations more easily accessible, and provide real-time services to travellers.
Applications range from AI systems that facilitate multi-language translation services for international travellers, to enhancing general safety and security via AI enabled video analytics.
In particular, Nextcontinent’s researchers emphasise the potential of translation services – which are being implemented at stations to facilitate real-time communication between passengers and station staff. Technology, which might be familiar to those who have used Google Translate in conversation, includes systems supporting multiple languages, which can display translated text automatically – helping people to feel comfortable on trains wherever they are in the world.
Pointing to other practical examples of technology reshaping the rail transport sector, the researchers highlight the Ideal Train Profile (ITP) of Indian Railways. Having been in development and testing since 2021, the ITP was introduced in early 2023. With its release, the AI-enabled system first analyses historical data, such as previous passenger bookings, origin-destination patterns, and train route demand, to understand and gain insight into complex demand patterns.
Boosting the capacity for which a station or rail network can offer support to customers, the data allows the AI to accurately forecast passenger demand and determine the optimal seat distribution strategy per train. At the same time, the real-time data analysis allows for ITP to automatically adjust accommodation quotas – something which is especially useful to meet customer expectations during peak seasons.
Elsewhere, another example came from the UK, where HS2 is using digital twinning to enhance its emergency preparedness. By simulating emergency response protocols, the rail network – which is still under construction – can plan how to best aid passengers during a crisis – via a risk-free virtual scenario. Among others, this can help explore preparedness for a derailment scenario, with the technology allowing HS2 to simulate the immediate impact on passenger safety, evacuation procedures, and coordination with emergency services.
As well as having important impacts on passenger and staff welfare, the Nextcontinent experts believe the simulations will have major benefits for rail companies. The simulations are expected to slash incident response times by up to 40%, ensuring that real-world emergencies are managed swiftly and effectively, also improving reputational outcomes for rail companies, and reducing the amount of money other kinds of physical testing would incur.
Boosted rail operations
Operationally, rail companies are leveraging technology to streamline many of their core functions – as well as identify areas for savings. Leveraging advanced data analytics, IoT devices and AI can significantly boost productivity, operational efficiency, and safety.
According to Nextcontinent, most prominently, this is being seen with the integration of data and technology in rail maintenance. Across the industry, the researchers note that diagnostic and condition data from trains is already being transmitted for disposition and vehicle maintenance planning. Maintenance depots themselves are even becoming more digitised and automated, using IoT sensors for monitoring the condition and location of machinery and equipment, automated diagnostic and maintenance systems. Coupled with robotic technologies for performing maintenance and repair tasks, this allows for efficient and precise maintenance, reducing downtime and optimising the overall maintenance process.
The researchers contend that this kind of predictive maintenance could increase the reliability of rail services by up to 15% and reduce maintenance costs by 20%. But moving further, AI can be used to optimise energy output and minimise resource waste for electric or fuel-powered trains – with operators already attempting to train AI to consider various factors for optimisation operation, from passenger load and demand, to weather patterns, and even track conditions during irregularities.
An example of this kind of application was first seen in 2022, when Singapore’s SMRT Corporation successfully trialled an AI system at two stations, which worked to optimise cooling and reduce energy consumption by approximately 7,000 megawatthours annually. According to Nextcontinent, the AI system accurately determined the energy output needed to maintain a temperature of 26C, and automatically adjusted the air-conditioning systems based on station data, such as weather conditions and commuter foot traffic.
That is not to say that any of this is easy, though. The researchers also warn that integrating advanced technologies like machine learning and predictive analytics into existing railway systems poses several significant challenges, thanks to the complexity of railway operations, which involve multiple subsystems and components. This makes seamless integration difficult, while implementation of such technologies requires significant investment in sensor technology and analytics capabilities.
Even so, though, there are a growing number of successful implementations. To that end, Nextcontinent finally points to Siemens’ Mobility Data Services Centers in Munich-Allach, Moscow, and Atlanta. According to the study, these are prime examples of how combining engineering expertise with data analytics can optimise train operations – as the hubs use real-time data to perform condition-based and predictive maintenance, significantly reducing manual diagnostics and unplanned maintenance. As the sector continues to evolve, learning from best practices such as these, the researchers conclude that leveraging digital technologies will become a daily part of enhancing efficiency, safety, and ensuring operational excellence.
