Seconds to Safety: AI-Driven Solutions for Critical Infrastructure & Transit Incidents

Transit systems are heavily monitored, and don’t lack means of surveillance. Metros, railways and airports already rely on extensive camera networks covering platforms, tunnels, waiting lounges, gates and depots. The real challenge is converting this vast video data into immediate, actionable responses.

With thousands of cameras in operation, no control-room team can watch this volume of video feeds continuously. Yet, the most dangerous incidents often unfold in seconds: a passenger falls on the tracks, a person climbs onto a train roof, a bag is abandoned in a terminal, or a crowded platform becomes unsafe during a train delay. AI video analytics transforms the vast data stream by highlighting the specific incidents and responses that require immediate human intervention.

This technology is not designed to replace people. Its role is to identify hazardous situations, trigger immediate alerts, and help the right operator respond before an incident becomes a tragedy.

Four event types that demand immediate attention

Transit safety systems typically focus on a limited number of high-consequence scenarios.

People falling or entering the tracks are among the most urgent situations, since it’s a life-threatening emergency. A person may fall because of an accident, a medical emergency, or as a deliberate action. Detecting the event immediately can give control-room staff and train dispatchers time to stop or hold services and coordinate an emergency response, which is nearly impossible with manual monitoring.

Platform-edge crowding can become especially hazardous during service disruptions, when platforms are full of people. Crowd-density analytics can identify when passenger concentration is approaching a dangerous level, giving staff time to manage entrances, redirect flow, hold trains, or make public announcements.

Train surfing is another dangerous and often fatal behavior. People riding on the roof or outside a moving train may appear in a camera view for only a few moments. Automated detection provides continuous monitoring in situations that can be easily missed by human staff.

Unattended items pose both security and operational risks. AI can identify a bag or other object that remains stationary for longer than a defined timeframe and send an instant alert to an operator. This allows staff to focus on reviewing specific alerts rather than constantly scanning every camera feed themselves.

Training AI for rare events

The most important safety incidents are also the hardest to teach an AI system to recognize. They are the ones for which there is almost no data. Such incidents happen very infrequently, and collecting thousands of real examples of a person falling onto railway tracks or surfing on a train roof is neither practical nor ethical.

Since off-the-shelf computer vision isn’t enough for these critical applications, deployments rely on deeper expertise. Transport authorities help bridging this gap by sharing rare footage and operational knowledge. Meanwhile, developers use synthetic training to simulate rare incident scenarios by modeling diverse conditions like varied station layouts, lighting, different camera angles and crowd behaviors in 3D environments to better prepare AI for real-world incidents.

While standard computer-vision models rely on massive, labeled datasets, synthetic training offers a way to stress-test systems against real-world challenges, such as low visibility, crowded stations, or visual obstructions. This approach allows operators to distinguish between solutions that only perform well in controlled demos and those truly capable of operating in live, complex environments.

From alert to response

Detection alone does not improve safety. Alerts only provide value when they trigger a coordinated response. To be truly effective, an AI solution must do more than detect; it should instantly route alerts, display the relevant camera feeds, and notify the right personnel. This integrated workflow is critical to accelerating human response times during emergencies.

This integrated workflow also solves a major limitation in extensive camera networks: the number of monitors an operator can manage. According to data from transit partner MetroCCTV, IREX deployments allow a single operator to effectively oversee over 1,000 cameras, resulting in a massive improvement in response times over manual monitoring. While the cameras were already in place, AI fundamentally accelerated the speed of operational response.

Safety without unnecessary surveillance

Maintaining public trust in transit safety requires analytics that are both accurate and proportionate. A system intended to protect passengers should focus on identifying and responding to safety hazards, specific events and conditions, rather than monitoring ordinary commuters.

The system’s architecture must be designed to enforce this separation and restrict active monitoring to specific safety events only, while ensuring that all follow-up actions remain under human control, guided by strict verification, access protocols, and established procedures.

For transit operators, the ideal solutions are those that integrate with existing camera infrastructure, perform reliably in local environments, provide transparency on how rare events were modeled, connect alerts directly to response workflow, and have a core design that prioritizes situational awareness over individual tracking.

Transit networks operate at an enormous scale, and the window to prevent a tragedy is often measured in seconds. Cameras provide visibility across the system, but it is real-time AI that enables rapid response, while ensuring the essential role of human judgment in final decision-making.

About IREX

IREX is an ethical AI platform for smart cities and public safety, deployed across 8 countries and managing more than 250,000 cameras globally. The platform applies this type of analytics in operational transit environments, including world’s largest underground systems and airports, where systems monitor various potential emergency scenarios.