Moving people safely is one of the most regulated activities in the world. That makes aviation and rail cautious adopters of AI — but also sophisticated ones, with long experience of automation and data. Here is where AI is used today and how safety regulators are approaching it.

Aviation

Maintenance

Modern aircraft engines and systems generate large volumes of sensor data. Airlines and manufacturers analyze it to predict component failures, plan maintenance and reduce delays caused by technical problems. Computer vision helps inspect engines and fuselages.

Operations

AI helps airlines with:

  • crew and aircraft scheduling;
  • fuel-efficient flight planning;
  • predicting delays and managing disruptions;
  • turnaround optimization at gates;
  • dynamic pricing and revenue management, long a data-driven field.

Air traffic management

Research and early deployments use AI to forecast traffic flows, support controllers with conflict detection and optimize arrival sequences. Controllers remain responsible for separation.

Certification

Safety-critical aviation software must be certified. Machine learning challenges traditional methods because its behavior is learned from data rather than specified line by line. The European Union Aviation Safety Agency (EASA) has published an AI roadmap and guidance for machine learning applications, with requirements on learning assurance, explainability and human–AI teaming — starting with lower-risk assistance functions.

Rail

  • Predictive maintenance of trains, tracks, switches and overhead lines using sensors and inspection trains with cameras;
  • timetable and capacity optimization;
  • energy-efficient driving advice for train drivers;
  • automation: many metro lines already run driverless using conventional automation; AI is being tested for obstacle detection on mainline trains.

Public transport

  • forecasting passenger demand to adjust frequency;
  • real-time arrival predictions;
  • on-demand bus services with routes computed dynamically;
  • journey planners that combine modes;
  • analyzing crowding for safety and comfort.

Airports and stations

Facial recognition for boarding and border control is used in some airports, raising privacy debates. AI also manages baggage flows, security screening support and passenger flow predictions.

Risks

  • certification and verification of learned systems;
  • cybersecurity of connected transport systems;
  • over-reliance on automation and loss of manual skills;
  • privacy in biometric systems;
  • fairness in access to on-demand services.

Human–AI teaming in the cockpit and control room

Aviation has decades of experience with automation, and one lesson stands out: automation changes the human’s job rather than removing it. Pilots and controllers must understand what automated systems are doing, notice when they behave unexpectedly, and take over smoothly. Accident investigations have repeatedly identified confusion about automation modes as a contributing factor.

This experience shapes how regulators approach machine learning. EASA’s guidance describes levels of AI involvement, from assistance to the human, through human–AI cooperation, to more autonomous functions under human oversight. Early approvals focus on the first level: tools that support decisions, with humans clearly in charge. Higher levels will require much stronger evidence that systems behave safely across all foreseeable conditions.

A practical example: predictive maintenance in a fleet

An airline fleet produces continuous engine and system data. A typical predictive maintenance program:

  1. collects data from aircraft systems after each flight;
  2. compares it with models of normal behavior for each engine type;
  3. flags trends — for example rising temperatures or vibration on one engine;
  4. lets engineers inspect the component during a scheduled stop rather than after a failure causes a delay or diversion;
  5. records the findings to refine the models.

The benefit is measured in fewer technical delays and cancellations, better spare-parts planning and longer component life — all without the AI making safety decisions on its own.

What to watch next

  • EASA and other regulators approving more machine learning applications.
  • AI-based obstacle detection for automated mainline trains.
  • Single-pilot cockpit proposals and the debate around them.
  • Biometric boarding rules and privacy protections.

Key takeaways

  • Aviation uses AI for maintenance, scheduling, flight planning and disruption management.
  • EASA has published an AI roadmap and machine learning guidance focused on learning assurance.
  • Rail uses AI for maintenance, capacity planning and energy-efficient driving.
  • Public transport uses AI for demand forecasting, arrival predictions and on-demand services.
  • Certification, cybersecurity and privacy are central challenges.

Frequently asked questions

Do planes fly with AI?

Autopilots and flight management systems are conventional automation, not machine learning. ML is mostly used in maintenance, operations and assistance tools, with certification efforts underway.

Are driverless trains common?

Many metro lines worldwide run without drivers using established automation technologies. Mainline driverless trains are less common.

Is airport facial recognition mandatory?

It depends on the airport and country. Some programs are optional; rules on consent and data retention vary.