Five reasons AI makes sense for the rail industry
Artificial Intelligence (AI) technology has led to a number of notable improvements in efficiency and safety across rail networks, as well as significant cost reductions. One of the biggest driving forces behind this is the willingness of the industry to collaborate with forward-thinking start-ups.
Here are just five of the notable improvements AI has delivered for the rail industry, and some of the truly innovative start-ups leading the charge.
1. Predictive maintenance
With the ability to quickly process vast amounts of data, AI algorithms can identify patterns that indicate when equipment is likely to fail.
Start-ups such as Amygda use data-fuelled AI to implement predictive maintenance platforms, aimed at reducing unplanned downtime and extending asset life. Predictive programmes such as this can also allow for proactive maintenance, helping to prevent costly downtime.
2. Improved safety
AI has opened up a number of fresh ways to ensure safety in the rail industry. By analysing data in real-time, systems can detect potential hazards and alert operators. For example, AI-powered cameras can monitor track conditions and alert operators to any abnormalities, preventing derailments.
A great example, and a previous winner of one of Lab by Transport for Wales innovation programmes, is RoboK. The start-up developed an efficient AI-based computer visual insight solution to democratise safety in transportation.
3. Enhanced capacity utilisation
Efficiency is a key KPI for rail companies and is one of the areas AI can excel in. By analysing data on passenger demand, train schedules, and track availability, AI algorithms can determine the most efficient use of resources. This can minimise delays and improve the overall passenger experience.
A great example of this can be found in the solution Signalbox delivered as part of LNER’s flagship innovation programme, Future Labs. The start-up developed a minimal viable product (MVP) that uses technology to detect, track and map trains across the UK, using personal devices.
4. Enhanced passenger experience
AI can also enhance the passenger experience in the rail industry. By analysing data on passenger behaviour, algorithms can predict passenger demand and optimise train schedules, ensuring that trains always arrive on time.
As participants in LNER Future Labs 3.0, 4Roads used a process called ‘simultaneous localisation and mapping’ (SLAM) as the basis of their exciting solution to improve wayfinding in complex environments, such as train stations. The initial idea was to support visually impaired travellers, but it was quickly found that the technology could benefit multiple rail passengers.
5. Increased operational efficiency
In a similar vein to using algorithms to enhance the passenger experience, AI can also improve operational efficiency in the rail industry through the analysis of potential delays or downtime. As well as monitoring machinery, AI technology can even be used to monitor staff for potential injuries, which can cause significant delays to projects.
A great example of operational efficiency is the winner of Lab by Transport for Wales second cohort, Spatial Cortex. As part of the cohort, the team developed an MVP for a wearable bio-mechanical monitoring system to monitor workers for manual handling injuries.
These are just some of the ways that AI is already benefitting the rail industry. As technology continues to evolve, we can expect to see even more examples of how smart technology can make rail travel safer, more efficient, and more comfortable than ever.
What do you think is the best example of AI use in rail?
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