This case study presents initial findings from the deployment of Assaia’s ApronAI, Turnaround Control solution at one of the fastest growing airports in the US. Airports’ growth brings typical challenges related to capacity. The examined airport is a classic case because there is little room for physical expansion and the only options that are available are extremely costly as major infrastructure works are required.

As a result of limited capacity and high traffic, the airport can get very busy, especially at peak hours. One result of this has been that inbound flights often have had to hold before their gate becomes available. The preferred location to hold an aircraft is in the movement area between the two parallel runways. If it is known that the target gate of an arriving aircraft is not yet available, the ramp control tower thus asks Air Traffic Control (ATC) to hold the aircraft there until the gate becomes available. Then the flight is cleared to enter the non-movement area and is guided by Apron Control to its gate.

However, if information about gate availability is not known in advance, a flight might be directed towards the non-movement area even though its gate is not available. As there is no space in the non-movement area for holding, in this case Apron Control lets the aircraft move around the non-movement area not to block any other traffic. This means that an inbound plane is taxiing in circles until its gate becomes available. This procedure obviously increases aircraft fuel consumption and thus imposes unnecessary cost to the airlines as well as negatively impact airport sustainability and noise levels. Furthermore, this procedure also often includes handovers of the control of the aircraft between Apron Control and ATC. In an already busy situation this introduces additional workload for traffic controllers and therefore brings unwanted risk.

Set Up

Besides helping airlines to enhance their on time departures through real-time insight into ongoing turnaround operations, the Turnaround Control solution also provided a solution to reduce taxi times of inbound aircraft.

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On the 19th of May 2021, the airport took the Turnaround Control solution into production for 44 gates. The map view of the application shows the users in the ramp tower a real-time overview of gate occupancy. Furthermore, by clicking on any of the gates the apron controllers can get a detailed view of the turnaround progress and estimate how much time is left before the aircraft will leave the gate. This information, which was not available before, gives the controllers a better understanding of which gates will be available when. It enables them in advance to decide which aircraft should be held between the runways in the movement area and which aircraft can directly enter the non-movement area to taxi to their gate.


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This implies, with more aircraft being held in the movement area, that less aircraft enter the non-movement area while their gate is not available. And thus reduces the cases where aircraft are taxiing around and unnecessarily burning fuel and keeping ground controllers busy with handovers.

Even though the first release of the Turnaround Control application already made a big impact (see next section), the best is yet to come! In October 2021, during the second release, the solution was rolled out across another 21 gates covering almost the entire airport. Furthermore, the Predicted Off-Block Time (POBT) feature will be implemented too. The POBT is a prediction that uses a wide range of data sources in order to continuously predict when an aircraft is going to go off-block. This proactive and predictive information will further enhance Apron Controllers’ real-time insight and enable them to make even better decisions than before.

Results

In order to validate and quantify the positive impact of the introduction of the Turnaround Control solution, we investigated taxi-in times of arriving aircraft. Better insight into gate availability should lead to less aircraft being allowed into the non-movement area and spending time there taxiing around. We found that shortly after the Turnaround Control solution had been implemented, average taxi-in times decreased.


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Figure 3 shows the average difference in taxi-in times between Assaia and non-Assaia gates over time. Since many external factors can influence the taxi-in times, we have chosen to take the taxi-in times to non-Assaia gates as a baseline and show the difference with Assaia gates. You can see that before the implementation of the Turnaround Control solution, taxi-in times were higher at the Assaia gates (hence the positive values). After the implementation, this difference decreased.

In order to quantify the results, we compared taxi-in times for aircraft headed to gates where the system is active versus gates where the system is not active. In line with our expectations, we found that whereas taxi-in times increased for aircraft heading to non-Assaia gates, they decreased for aircraft heading to Assaia gates.

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Figure 4 shows the difference in average taxi-in time before and after the system’s implementation for both groups of gates.


Again, if we take the taxi-in performance on non-Assaia gates as a baseline we can conclude that the introduction of Turnaround Control reduced taxi-in times on Assaia gates with 49 seconds.

Based on average aircraft operating costs as provided by the FAA this reduction in taxi-in times results in a cost saving for the airlines of $122 per flight, or an annual total saving of more than $25 million for all flights at the examined airport. Furthermore, these reduced taxi times also reduce CO2 emissions by 13kg per flight or 2.6 million kgs of CO2 per year.

If you are eager to learn more about this particular case study or about different ways in which Assaia’s ApronAI software can create value for your airport or airline contact us at info@assaia.com or via the website form.

Testimonials

The use of Assaia's TurnaroundControl has provided our Airline Partners, Ground Handlers, and Service Providers with full visibility of the aircraft turn processes. The data, predictability, and visibility enhances our collective efficiency and resilience at Toronto Pearson. Paired with our A-CDM initiative, Assaia's TurnaroundControl provides crucial data driving A-CDM processes, a reduction in turn times, and a noticeable increase in on-time performance.

Dean Wright
Associate Director, Gating & Airport Flow

The way Turnaround 2.0 uses AI and machine learning to boost our zone controllers' efficiency is remarkable. They can now manage multiple gate activities with more focus on handling exceptions, rather than multitasking. Turnaround 2.0 is more than a tool for the present; it's a foundation for 'United Next,' growing with us and helping us surpass our operational goals efficiently.

Daniel Reed
Director of Station Operations, United Airlines

Our focus is to use innovation to make our operations smoother. We have selected Assaia because of the capabilities of the tool. But it is also really important for us that it has a good record of successful implementation, so we know it will deliver for us, for our airlines and ultimately for our passengers.

Dr Babett Stapel
Managing Director, Fraport Slovenjia

We are optimizing all our processes on the apron to shorten the time each aircraft needs to be on the ground, which benefits both our passengers and our airline partners. This is a common issue across our airports and we are talking to all of them about this technology.

Claus Grunow
VP of Corporate Strategy and Digitalization at Fraport

We are pleased to partner with Assaia to implement the ApronAI Turnaround Control solution at T4. This new solution will not only optimize operations and our work with our business partners, but will also help us to ensure a first-class customer experience at T4.

Roel Huinink
President and CEO, JFKIAT

For most airports, the apron is a a black box. Assaia finally gives our ground staff full insight into every turnaround. This allows them to focus on what really matters, while simultaneously making the work environment safer.

Jason Aspelund
Former Manager Strategic Performance, Alaska Airlines

The real-time and historical insights that can inform both airport and airline operations make this solution a clear winner for everyone.

Craig Paul
Director of Technology & Innovation , Halifax Stanfield International

Assaia's product allows airports and airlines to collect, track, and analyze data in real time; this innovation removes inefficiencies and optimizes performance.

Jim Lockheed
JetBlue Ventures

We’re creating the airport of the future, and innovation in apron operations will directly improve the passenger experience. We are laser focused on innovations that will make Pearson and its whole apron ecosystem more efficient while reducing our carbon footprint.

Deborah Flint
President and CEO GTAA

SEA needed an innovative solution to our capacity problem and have worked with Assaia to optimize the turnaround process resulting in reduced taxi times and increased passenger satisfaction. Assaia has exceeded our expectations, consistently delivering on-time & on-budget.

Samer Tirhi
Airline Scheduling Coordinator, Seattle-Tacoma International Airport

With the help of Predicted Off Block Time from Assaia. JFKIAT Operations can be proactive to reduce or eliminate any delays and gate holds

Stephen Tukavkin
VP IT & Digital, JFKIAT

I had mentioned before, great innovation on your part. With these types of improvements, T4 is always leading at JFK. Thank you

COPA Station Manager
JFKIAT

We are proud to be partnering with the Assaia team in our mission to use technology to improve the efficiency and safety of the airport environment.

Raghbir S. Pattar
Director of Airports Transformation, IAG

We’re working hard on becoming an airport of the future, and this involves rethinking every part of our ground operations. Assaia’s ApronAI is an integral component of our vision for the ramp of the future.

Abhi Chacko
Head of Innovation & Commercial IT Services, Gatwick Airport

Assaia’s technology adds critical data points to CVG’s early-stage neural network for operational advancements. Structured data generated by artificial intelligence will provide information to make decisions, optimize airside processes, and improve efficiency and safety.

Brian Cobb
CIO, CVG Airport

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