Digitalisation in public transport has reached a new stage of development. While digital passenger information, electronic fare collection systems and computer-based operations control systems have become established standards, artificial intelligence is now moving to the forefront as a technology capable of fundamentally transforming existing systems. InnoTrans 2026 in Berlin made one thing particularly clear: artificial intelligence is no longer merely a topic for the future of public transport. It is beginning to take on concrete tasks in planning, operations, maintenance and customer communication.
With 414 exhibitors, the Public Transport segment was more international and diverse than ever. Particular emphasis was placed on the Mobility+ section and the AI Mobility Lab, first introduced in 2024. The latter brought together 47 companies presenting applications in artificial intelligence, automation, cybersecurity and robotics. At the same time, it became clear that digitalisation in public transport is no longer limited to individual AI applications. Instead, an increasingly interconnected digital ecosystem is emerging in which vehicles, infrastructure, transport operators and passengers are linked through data.
AI becomes part of operations control
One of the most important developments concerns the traditional task of controlling public transport operations. Every day, transport operators have to coordinate drivers, vehicles, schedules and infrastructure while responding to delays, disruptions and short-notice changes. This is precisely where data-driven systems can demonstrate their strengths.
At InnoTrans, INIT presented solutions integrating artificial intelligence and other data-driven technologies into public transport operations. The aim is not to completely replace the dispatcher or driver. Instead, intelligent systems are designed to analyse large volumes of data, identify correlations and provide recommendations for action.
Their potential lies particularly in their speed and ability to take numerous factors into account simultaneously. Historical operating data, current delays, vehicle positions, passenger demand and disruption reports can be combined. This makes it possible, for example, to identify deviations from the timetable at an early stage and prepare appropriate countermeasures.
Transport planning is also increasingly supported by algorithms. Instead of relying solely on a fixed timetable based on historical experience, digital systems can take actual demand and the current operating situation more fully into account. The emphasis is therefore shifting from simply processing data towards predictive operations management.
From ticket to digital travel companion
A second major development concerns the relationship between passengers and transport operators. The traditional transport ticket is gradually becoming invisible.
At InnoTrans in Berlin, FAIRTIQ demonstrated its pay-as-you-go system, in which passengers start their journey using a smartphone and the fare is calculated automatically. At the same time, the application is evolving from a pure ticketing system into a digital travel companion. Since 2026, FAIRTIQ has been integrating timetable information, real-time departures, platform information and delays into its app, for example.
What is particularly interesting is the growing convergence of journey planning and ticketing. In the future, passengers may no longer need to switch between a timetable app, ticket machine, transport association app and real-time information service. Ideally, a single digital interface will provide access to the entire journey.
The technical infrastructure behind this is also changing. FAIRTIQ, together with RMV and the Czech transport organisation IDZK, is working on the “BIBO Next” project to develop a software-based version of the Be-In/Be-Out principle that does not require additional beacons in vehicles. The aim is a scalable approach to seamless ticketing.
This illustrates an important point: digitalisation in public transport does not necessarily mean more visible technology. On the contrary, the best digital solution may be one that passengers barely notice because it operates entirely in the background.
Intelligent ticketing and revenue protection
Stationary infrastructure is also becoming increasingly digital. Scheidt & Bachmann presented new generations of ticket vending machines with its FareGo ST|32 and ST|72 systems. The traditional ticket machine is evolving from a simple sales device into a multifunctional service point.
Particularly interesting is the growing integration of ticketing, communication and access control. Scheidt & Bachmann’s FareGo PG|50, for example, uses a sensor system designed to distinguish between passengers, luggage and potential fare evasion attempts.
Artificial intelligence and intelligent sensor technology are therefore also becoming tools for revenue protection. For transport operators, this is an important aspect: digital systems not only have to offer greater convenience, but also have to operate economically.
Accessibility becomes data-driven
Another major field for digitalisation is accessibility. GoodMaps demonstrated solutions for highly precise navigation, particularly aimed at supporting people with difficulties in orientation.
This highlights one of the key advantages of digital systems. Traditional passenger information is largely based on static maps, signs and announcements, whereas digital navigation systems can provide individualised routes and connections.
For public transport, this opens up new possibilities – for example, when navigating through large railway stations or linking stops, platforms and entrances. Digitalisation can therefore improve not only operational efficiency but also the actual accessibility of the transport system.
When vehicles start to act autonomously
Automation of railway operations goes a step further. At the AI Mobility Lab, among others, OTIV demonstrated applications for driver assistance, remote control and autonomous operation in rail transport.
It is important, however, to distinguish between different levels of automation. Not every AI application means that a vehicle will operate without staff. Driver assistance systems may initially support specific tasks, while remote control and highly automated driving represent further stages of development.
This evolutionary approach is particularly interesting in rail transport. Autonomous technologies could initially be deployed in clearly defined operating situations – for example in depots, stabling yards or specially protected sections of line. Only later might their use be extended to more complex operational environments.
RailEvo also presented a concept for autonomous on-demand rail taxis at InnoTrans. The idea is to enable a more flexible form of rail transport in which smaller autonomous vehicles could be deployed according to demand.
Robots as the new employees of infrastructure maintenance
It is not only vehicles themselves that are becoming more intelligent. Maintenance and inspection are evolving as well.
Scout Robotics presented autonomous robotic solutions for inspection tasks in railway operations at InnoTrans. This highlights another important aspect of digitalisation: machines are increasingly taking on tasks that were previously carried out manually or with conventional measurement systems.
The key advantage is not automation alone. Sensors can continuously collect data while AI-based systems analyse large quantities of information. A single inspection can therefore potentially evolve into continuous condition monitoring.
For transport operators, this could lead to a shift from reactive to predictive maintenance. Instead of a defect triggering maintenance, the predicted probability of a failure could become the trigger for intervention.
Cybersecurity becomes a prerequisite
However, the more closely vehicles, signalling systems, operations control, ticketing and passenger information are interconnected, the larger the potential attack surface becomes.
Cybersecurity was therefore an integral part of the AI Mobility Lab. Companies such as FORTINET were among the exhibitors, alongside providers specialising in data protection and secure IT infrastructures.
Digitalisation in public transport cannot therefore be considered in isolation. Every additional interface, cloud service and connected vehicle must also be regarded as a potentially security-relevant component of the overall system.
This presents a particular challenge for transport operators with long vehicle lifecycles. A tram or regional train can remain in service for 30 years or more, while IT technologies may change within just a few years. Future-proof systems therefore need not only to be powerful, but also to remain updateable throughout their operational life.
From digital to learning transport systems
Perhaps the most important conclusion to emerge from InnoTrans 2026 lies not in any individual world premiere, but in the increasing interconnection of different technologies.
Digital tickets generate data about journeys. Vehicles transmit operational information. Sensors record the condition of components. Passenger information systems know about delays and connections. Operations control systems analyse network performance. AI can bring these different sources of information together.
In the long term, this could create a transport system that no longer relies exclusively on predefined rules, but can increasingly adapt its decisions to the current situation.
That does not mean that every decision should be left to an AI system. In public transport, transparency, data protection, functional safety and human responsibility remain essential requirements. AI must therefore be understood as a tool operating within a clearly defined system architecture.
InnoTrans 2026 demonstrated impressively that this development has already begun. From invisible digital ticketing and AI-supported transport planning to intelligent passenger information, autonomous vehicles and robotic systems, digitalisation is increasingly moving out of the background and into operational processes.
The next stage in the development of public transport will therefore be shaped less by a single new vehicle type than by the ability to connect data, vehicles, infrastructure and people into an intelligent overall system.
The public transport system of the future will therefore not only be electric and connected. It will increasingly be data-driven, predictive and capable of learning.
02.10.2026
