How to manage passenger flows in airports

After nearly two years of COVID disruption, airports have come back to life over the past few months, and planes are once again full. Air traffic is finally recovering after an unprecedented crisis in the history of commercial aviation.

However, this recovery has not come without challenges quite the opposite.

Many airports reduced their workforce to cope with the sharp decline in air traffic during the health crisis. Yet the rebound has been much faster than expected, defying all forecasts that predicted a return to normal only by 2024 or even 2025.

As a result, queues have grown excessively long at check-in counters, security checkpoints, passport control, and even baggage claim areas across the majority of major European airports, creating widespread disruption.

Beyond the need for effective human resource management to ensure smooth airport operations, the question of optimizing passenger flows at key touchpoints throughout the journey has become critical.

In recent years, artificial intelligence and machine learning have opened new possibilities in the field of people detection and counting.

By leveraging neural networks and specialized learning algorithms, it is now possible to detect and count people in real time using video streams from surveillance cameras. These new algorithms offer high accuracy in both indoor and outdoor environments.

AI applied to computer vision introduces a new approach, enabling detection and counting using more accessible and cost-effective data capture technologies namely, standard 2D video cameras.

Combined with the continuous increase in computing power of modern computers and microcomputers, computer vision is poised to become a key technology for passenger counting and queue management in airports.

At Acorel, this is precisely what we are working on and our initial results are already very promising.