Airports turn to artificial intelligence and wider integration for predictive queueing optimisation

As the desire from airport operators, airlines and above all passengers to have an efficient and predictable journey through the airport grows, airport technology providers are turning to artificial intelligence and a growing range of information sources to make terminals more efficient. We dive into some of the new technologies and implementations in the field, taking a look at the latest updates and trends coming from market leaders in this field.

At the core of the latest generation of technology is transformation: adding new real-time technology sources like lidar sensors and live 4K cameras, processing the information they provide, adding it to a disparate set of data, and enabling airport managers and executives to make better decisions — all while respecting passengers’ privacy.

One of the leaders in the field is Veovo, whose Passenger Predictability technology is being used at airports ranging from Leeds Bradford in the UK to the two main Argentinian airports in Buenos Aires, as well as most recently Budapest Airport in Hungary. 

At its core, this passenger flow analytics platform is designed to help airports and security providers allocate their staff and checkpoint resources more efficiently, as well as to reduce bottlenecks and provide passengers with the maximum possible information. 

Using the cloud below the clouds

Most recently, the company has created the option of rolling this technology out onto a cloud-based technology stack, responding to the importance of scalability for this and other kinds of passenger flow analytics. This stack can mesh together with a terminal digital twin, creating real opportunities to unlock the benefits of intelligent, technology-enabled airports.

“The AI-powered cloud software measures passenger movement and offers live and predictive insight into customer behaviour. By aggregating data from multiple sensors and data sources, the analytics platform delivers valuable metrics such as occupancy, predicted wait times and lane productivity,” Veovo says.

Live queue time, as well as predicted wait time information, will help passengers to plan their airport journey more effectively. Displaying live wait times on screens means passengers can adapt their airport experience to what’s happening real-time, either speeding up their airport journey or slowing down to enjoy the experience (and indeed contribute to those key non-aviation revenue streams).

Another field leader is CrowdVision, whose movement tracking software is focussed on cameras, using overlapping sensor infrastructure includes both cameras and lidar laser-ranging to get a broad view of spaces. This can generate useful insights around inflows and outflows, queues, wait-times, asset utilisation (like desks and kiosks), processing times, occupancies and densities.

Beyond the processing of airport formalities, this kind of technology can also help airports to make strong investment and partnership decisions. Is there significant throughput at a certain coffee shop or sandwich place throughout the day? That’s hard data that the airport operator can use to attract new outlets and persuade existing vendors to improve their offerings.

The latest generation means integration, integration, integration

But inputs to many of these systems can be multiple and varied. One set of live visuals and presence data comes from lidar technology and stereo cameras, which can be processed into a real-time picture of a terminal space. Another set comes from passenger personal electronics, including using wifi probe requests and Bluetooth low energy beacons to count the number of passengers within an area. Yet another set are data sources from the rest of the connected airport: the knowledge that a widebody of 350 people is going to arrive in ten minutes at a gate five minutes walk away from the start of the queue, for example.

This integration of existing sources of information is key to making the most out of existing capital and hardware investments — and figuring out how to best integrate them elsewhere through APIs or other data streams is critical.

“Budapest Airport will use data from multiple movement sensors and machine learning to understand passenger behaviour, predict the impact of events, and make informed, proactive decisions,” Veovo explains. “The airport also plans to expand the technology from kerb to gate to benefit passengers throughout their airport journey.”

The technology stack includes an impressive set of dashboards, allowing a variety of pieces of information to be combined so that decisionmakers can manage the situation in real-time. For example, for a major airport’s arrivals process, the information on the number of passengers queueing and their expected wait time can be split across international arrivals using passport e-gates and those using staffed checkpoints. Similarly, in the baggage hall, the system can count the number of passengers at particular belts, give an estimation of how much time they’ve been waiting, and enable the airport to divert other flights’ baggage to other belts in the event of any delays before they start piling up.

“Veovo’s cloud platform is scalable and allows for initial implementation at the optimal point of impact, expanding to include measurement, prediction and planning in other areas as they are identified,” says the company. “For example, Budapest Airport began using the technology at security and check-in and now plans to use it to forecast passenger show-up profiles and build efficient lane opening plans to match demand.”

This kind of growing integration can enable the further addition of other technologies, including virtual queueing, provided by a range of companies including Pangiam and Qtrac. This new option, largely mainstreamed during the COVID-19 pandemic, is beneficial for a number of reasons: smoothing peaks and troughs of passenger processing, enabling fast-tracking of premium class passengers or valuable frequent flyers, reducing dwell times, allowing more efficient use of limited space or processing infrastructure, and growing retail spend since customers can relax with a drink instead of standing in a queue.

At the end of the day, explains the Argentinian airport operator’s general manager for Ezeiza airport Sebastián Villar Guarino, “This technology allows Aeropuertos Argentina 2000 to have complete passenger flow visibility, helping us make informed decisions to deliver a better quality passenger experience.”

Author: John Walton
Published 18 April 2023
Feature image: With thanks to Róbert Baranyi, Official Photographer, Budapest Airport

 

One response to “Airports turn to artificial intelligence and wider integration for predictive queueing optimisation”

  1. Tony Abanga says:

    Great concept that can mitigate safety and security incidents as linked to passenger unusual behaviour or medical emergency circumstances.

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