When railways appeared in the 19th century, they stood for one of the most innovative sectors of the economy and a key player in the industrial revolution. However, with the rapid growth of road and air transport in the next century, they gradually lost their position as technological leaders. Since the 1990s, the advent of the Internet, the Internet of Things and big data has offered the railway sector a new opportunity for renewal. The vast amount of data generated can function as a fundamental tool, enabling companies to reform their organizational structure, improve performance and generate new added value. The adoption of artificial intelligence (AI) has become crucial to reap the full benefits of digitization, improving management, reducing costs and increasing competitiveness compared to other modes of transport.

Autonomous Driving for Trains of the Future

A significant example of the use of AI in railway technology is the contribution to the automation of train operations (Automatic Train Operation, ATO). This transfers the responsibility for managing operations from the driver to the train control system, with varying degrees of autonomy. Progress has been particularly clear in densely populated urban areas, with driverless metros and light rail transport. Although AI lends itself well to driverless driving in controlled environments, such as subways, progress is also being made in open spaces, where it is necessary to respond quickly to sudden dangers. AI and autonomous driving are also useful for improving the performance and competitiveness of rail freight transport, with projects underway to synchronize the movements of container trains on the network.

Predictive Maintenance and AI

Another crucial use of AI is in predictive maintenance, which finds patterns, anomalies or signals that may show a probable future failure or need for maintenance. This approach allows interventions to be planned in a targeted manner, reducing train disruptions and associated costs. For example, the Brazilian company Rumo has successfully introduced AI for predictive maintenance, reducing false alarms and increasing the efficiency of train services. SNCF highlighted how this technology has helped reduce accidents on rail switches and predict potential problems on catenaries.

Optimized Traffic Management

AI can effectively manage the large amount of data produced by the railway infrastructure, making real-time decisions on train passage planning. For example, Deutsche Bank has implemented AI-based systems to resolve conflicts between trains, reducing overall delays. In Italy, Trenitalia presented the Sentinel I.T. project, a train with AI and sensors to prevent train theft, while Ferrovie dello Stato explored the use of AI in customer service and customized travel experiences. Project, a train with AI and sensors to prevent train theft, while Ferrovie dello Stato explored the use of AI in customer service and customized travel experiences.

The Future and Challenges

Despite considerable progress, the railway sector faces major challenges, including the need for greater cybersecurity and managing the transition to less human presence. Overcoming these challenges will be essential to exploit the full potential of AI and further improve the efficiency and reliability of rail transport.