Atkins’ future borders technical lead, John Drever, explains how data modelling can reduce queuing times at airports.
As transit infrastructure like airports, train stations and borders become busier and more complex, our approach to designing public spaces with security in mind takes on another dimension.
However, advances in technology can make these spaces easier to transit, not harder. By optimising our infrastructure for the digitally-enabled passenger, we can even design-out unnecessary queuing – something more important than ever in a post COVID world.
To reduce queueing times and improve security, airports are introducing more and more digital technology alongside their existing infrastructure. This includes the likes of 3D baggage screening, body scanners, self-service kiosks and automated biometric identification, as well as online advanced passenger information, which can be completed before the passenger even leaves their home.
But, as the passenger experience and security are driven upwards, so too is the complexity of what needs to be done by people and computers behind the scenes. In short, it results in a three-dimensional set of design variables:
- Infrastructure – corridors, security lanes, bag-drops, scanners, check-in desks, etc
- Passenger and staff behaviour
- IT systems
So, how can we cope with these (often conflicting) variables, to design a system that reduces queues and improves security? The answer is with data modelling. Here are two techniques from the model-based systems engineering toolbox that can help design the optimised environment.
Method 1: Dynamic process data modelling and simulation
Simul8 – online simulation software – can be used to model the detailed flow of people through complex and secure infrastructure; in this case an airport.
Process modellers can use the tool to create a dynamic model covering the arrival, processing and transit of passengers through the security checking infrastructure, IT systems and human processes.
The software enables models to be fed with actual passenger numbers, processing times and arrival frequency from the airport in question to simulate an end-to-end journey process that mirrors the passenger experience for the airport in question.
It can also be used to conduct trials of new systems and configurations, to provide additional data and to prove the model. Having used this software for a client’s security lane in the past, some of the benefits included:
- Removal of bottlenecks in the passenger journey
- Optimising the security process
- Finding the most suitable position for new airport security gates
- Testing and rapid down-selecting of design options
- Predicting the impact of proposed regulation, process or equipment changes
- Accurately sizing the number of gates and fast-track lanes required
- Providing a data-driven design for a future security lane.
The beauty of this technique is that any future changes to the process or infrastructure (such as an increase in the percentage of passengers needing to be scanned or introducing body temperature scanners) can be easily modelled and designed for.
Security lanes aren’t the only part of the airport journey where this modelling technique can be used – this method is particularly applicable to analysing any complex queueing scenario.
Method 2: Passenger journey scenario modelling
In busy sites like airport terminals, border crossings and ports, space and time are at a premium. As such, optimum use needs to be made of the existing ‘touchpoints’ – the places where interactions already occur, such as bag-drop, check-in desks, security, for instance.
These touchpoints are becoming more complex and multifunctional such that passengers can check-in, drop their bags and enrol on the biometric security system all at a single place, to save time later in the passenger journey.
But with this efficiency comes added complexity behind the scenes, and airports need to be confident that all passengers and journeys can be managed within the airport system. Using this modelling method, all passenger transit scenarios can be captured to identify variables such as:
- All departures and destinations
- Whether passengers have enrolled in automated biometric identity
- Passengers with hold luggage
- How to manage exceptions, such as passengers revoking GDPR permissions part-way through the process.
Then, all the resulting scenarios can be organised into sprints and analysed, to:
- Ensure airlines, airport staff and IT managers all understand any passenger movement issues
- Clarify what infrastructure, software and hardware is required to enable the successful movement of passengers through their journey
- Provide a baseline of passenger movements that project managers and the software development team can work from
- Test new software in a safe lab setting before its trialled at the airport
- Demonstrate – and therefore enable the addressing of – security vulnerabilities at the design stage.
Testing and experimenting in the real world
After applying these two data modelling techniques to enhance the design robustness, there will still be uncertainties around how the holistic design will work live, with people in the physical environment.
The final piece in the jigsaw is to try out the designs in the real world and feed the results back into the models to improve them for the future.
Traditionally, systems are tested and demonstrated in the lab during each agile sprint, then follow a factory acceptance or site acceptance route to deployment. In busy environments where deployment is costly and time restricted, it is worth experimenting small parts of the system in the real environment early in the project lifecycle.
This allows the designers to check their assumptions on how the users and public will interact with the IT systems in the passenger journey at the airport, and help identify any challenges or issues early on in the process.