Leveraging airport capacity: how does technology help?

Every airport master plan involves capacity planning and an assessment of when airport resources will be insufficient to support increasing levels of airline demand. However, the operational teams charged with mitigating such capacity thresholds have a whole different process. And using the best tools can help you address capacity issues quicker.

At times, the balancing act of moving bottlenecks around the airport leaves no process unaffected. Once the gate capacity is raised, it should be balanced with availability of checkin counters and security throughput. Traditionally, airport planners look at planning the gate and runway capacity, but bottlenecks can occur throughout the terminal, on the landside as well as on the airside. 

The good news is that technology, when paired with the right policies, can allow airports to stretch capacity without the need for major investments or the expansion of infrastructure. Does it sound realistic?

Assaia’s ApronAI Suite has proven effective in many worldwide airports at a variety of scales, enhancing OTP and elevating awareness of the actual times for turnaround operations, helping to realize new levels of gate utilization (traditionally a strained airport resource). 

In this article, we will look at different use cases where the assistance of software solutions such as ApronAI allowed airport operators to show satisfying results on their efficiency reports.

Mitigating delays and controlling gate availability

Delays are unavoidable, and keeping them to a minimum is one of the shared KPIs across all airport operators globally. This is also a shared goal by not just airports, but also their tenant airlines and local air traffic controllers. The various stakeholders make it even more complex to resolve. One step toward mitigating delays is to respond timely, but anticipating when an aircraft's ground time may exceed its scheduled duration is necessary beforehand. When you can create a shared understanding with all stakeholders about ground delays, it allows the entire ecosystem to respond accordingly.

Toronto Pearson International Airport (YYZ) was able to reduce the average taxi-in time by 44% after a full-scale deployment of ApronAI in 2023. A total of 106 gates are now being monitored, and alerts are generated to specific stakeholders during the turnaround process. 

When turnaround subprocesses (such as fueling or catering) are running behind schedule, the resulting data is used to highlight the situation so that prompt mitigation measures can be implemented to reduce delays. Early availability of predicted off-block time enables the operational staff of the airport to identify any gate conflicts to prevent incoming aircraft from having to wait for their gate to become available. 

As much as automation can offload tasks from employees, only the necessary alerts create a comprehensive overview of the turnarounds throughout the terminals or zones. As a result, airports and airlines are able to handle more flights with the same number of staff. 

In the case of Toronto Pearson, reduced ground delays on average by 4.2 minutes resulted in savings worth CAD$147,773 ($108,462) annually.

Balancing under the peak declared capacity 

Airport operators work under the pressure of stable year-to-year passenger growth (excluding the turbulent times of COVID-19) and tightening sustainability restrictions that apply certain limitations on runway usage, especially if it’s adjacent to residential areas, which most major airports indeed are.

At times, deft maneuvering is needed to stay on the curve of growing business expectations, legislation, and capacity limitations. Reaching capacity peaks is a precarious balance – reducing aircraft movements will lead to revenue loss, but is a stretch of capacity without investment in new infrastructure possible?

By taking the capacity constraints that are not under the control of the airport as a starting point, operators are able to identify areas that can be optimized with a much less weighty investment than expansion, leading to capacity risk mitigation.

When technology is there to back up human capabilities, a lot of basic functions are covered with much lower failure rates. When gates are freed up more predictably and a disruption occurs, such as weather, labor strikes, or maintenance, the allocation plan can be adjusted in real time, and operations continue at a stressless pace.

Thanks to AI-powered technology, operations planning can be optimized based on certain hard and soft rules. Having a mathematical brain at its core, such software is capable of creating a versatile gate allocation plan based on a vast array of data. With this, there can potentially be more turnarounds per gate per day, fully aligned with the resources and other capacity constraints. 

Assaia’s Stand Manager is an example of using the power of machine learning to the benefit of all aviation industry stakeholders. Airports are provided with a tool that includes a user friendly interface that replaces spreadsheets and outdated systems for stand allocation. The algorithm allocates aircraft and resources to ground movements based on tailored and adjustable parameters.

Proactivity for delay reduction

Keeping the delays to a controlled minimum on a normal operations day is a capability available to most airports, no matter the size. Even when there is no room for improvement from a managerial standpoint, technology once again helps streamline the processes and achieve even more impressive outcomes.

A case study for Ljubljana Airport (LJU) has shown that with the usage of ApronAI the operations team was able to dramatically reduce ground delays, which were identified as the following: 

• For early arrivals: the actual departure time minus the scheduled departure time;
• For late arrivals: the departure delay (actual departure time minus scheduled departure time) minus arrival delay (actual arrival time minus scheduled arrival time).
• Excluding: early departures, early arrivals, or flights that have a ground delay of more than 60 minutes.

In situations where ApronAI’s alerts were seen, the average ground delay was reduced by 4 minutes compared to flights where alerts were ignored. Moreover, the active use of the system helped reduce that delay even more. Turnarounds with acknowledged alerts were completed in an average of 2 minutes less. 

Ultimately, there was a 6-minute disparity in the ground delays for flights where the technology was employed and those where it wasn't, and flights that were actively monitored departed under the 5-minute delay threshold.

ApronAI alerts are triggered against a delay in an expected event or operation during the turnaround, such as catering or fueling. They encourage proactivity in the ops teams and help identify risks and delays as soon as the deviation is detected through the Computer Vision system.

Being aware during peak seasons

Whenever capacity issues are of major concern, for example, during peak seasons or bad weather days, ApronAI gives a pair of spare eyes to the flight dispatcher teams. Additional functionality that is available in the TurnaroundControl product allows airlines to monitor several turnarounds at the same time, grasping the progress and the most critical events from a bird's-eye view.

The information gathered in ApronAI can be integrated with other software using APIs, such as the Resource Management System (RMS) or data analytics dashboards, to produce additional insights and create a system that aids in the early detection of operational improvements.

Balancing allocated capacity with real capacity through dynamic adjustments and leveraging advanced technologies is crucial for maintaining operational efficiency and effectively meeting traffic demand. Despite the preference for fixed capacity numbers by network managers, the pursuit of dynamic capacity balancing remains essential for the future of efficient airport operations.

Contact us today to learn how we can help improve your operations and work towards enhanced capacity.