This case study presents findings from the deployment of Assaia’s ApronAI, Turnaround Control solution at one of the US airports. The solution is already deployed across 87 gates, using existing camera infrastructure. Soon it will be rolled out to the remaining gates for full, airport-wide, coverage.
For a long time, data availability and data quality have been an issue in aviation. Efforts like Airport Collaborative Decision Making (A-CDM) have been undertaken to address this topic and have already had great positive impact. However, even at airports using A-CDM principles, still many data points are manually generated or sensitive to human tampering. In this case study we will specifically investigate the Actual Off-Block Time (AOBT).
For many airlines, the AOBT also marks the moment from which point onwards aircraft crews get paid. Different airlines use different ways to measure this milestone. Some options include: all doors closed, brakes released or pushback started. In practice, what we see is that often this milestone is artificially fabricated to ‘put the crew on the clock’ even though the flight is actually not yet leaving. This means that airlines are actually overpaying their crews. Furthermore, it also means that incorrect data is used for departure metering, flight planning, on-time performance reporting and air traffic control purposes.
Especially as airlines are emerging from almost two years of COVID crisis, the focus is very much on cost control and return to profitability. Practices like the one described above negatively impact these goals and furthermore might create a competitive disadvantage for airlines who do not control these practices.
At the airport, the ApronAI Turnaround Control solution uses existing cameras and computer vision technology to create timestamps for all key turnaround processes. The easiest event for the system to detect is actually the arrival and departure of an aircraft at a gate. Since the system operates entirely automatically, no human intervention is required or possible. This implies that the system is impartial, objective and ultimately just reports what is happening at the gate.
The airport system is deployed in the cloud. All timestamps generated are saved into a database as well as displayed to different user groups via the Turnaround Control user interface and integrated into the airport’s Aerobahn Surface Area Management application. The database holding all the turnaround timestamps is linked to Tableau, a data analysis application. This application is then used in order to analyse operational performance and find areas for improvement. For the purpose of this study, Tableau was used to analyse the difference between the AOBTs as used by the airport and airlines today versus the actual AOBT as generated by the Turnaround Control system based on video.
The benefit for the airlines from switching to the Assaia generated AOBT timestamp is $23 per flight or over $4.5 million per year.
Using the Tableau data analysis software, we are analysing AOBT accuracy from May 19th 2021 (the moment the Turnaround Control solution was taken into production) to September 29th, 2021.
Firstly, we found that indeed there is a difference between the current AOBT (which is usually acquired from aircraft sensor data via ACARS) and the actual AOBT as recorded by the Turnaround Control system. The average AOBT inaccuracy for all flights is 180 seconds.
When we look closer (also see figure 1), we also found that there are actually large differences between the different airlines operating out of the airport. The difference between the best and worst performing airline is as much as 6 minutes on average!
Finally, we also found that the AOBT inaccuracy is much more prevalent on delayed flights relative to flights which depart on time. Figure 2 both shows the number of flights for which the difference between AOBT and the actual AOBT is more than 30 seconds as well as the average difference in seconds for both delayed and on-time departures.
Given the fact that crews in some areas of the world start to get paid from AOBT onwards, the fabrication of this milestone at the incorrect moment is a breach of procedure that has a direct negative cost effect for airlines. Table 1 shows the average cost for a crew across all aircraft types departing from the airport. Based on these costs, the benefit for the airlines from switching to the Assaia generated AOBT timestamp is $23 per flight or over $4.5 million 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 email@example.com or via the website form.
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.
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.
The real-time and historical insights that can inform both airport and airline operations make this solution a clear winner for everyone.
Assaia's product allows airports and airlines to collect, track, and analyze data in real time; this innovation removes inefficiencies and optimizes performance.
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.
This data provides the single source of truth covering all turnaround operations. It is, therefore, an integral part of our Airport Collaborative Decision-Making initiative.
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.
With the help of Predicted Off Block Time from Assaia. JFKIAT Operations can be proactive to reduce or eliminate any delays and gate holds
I had mentioned before, great innovation on your part. With these types of improvements, T4 is always leading at JFK. Thank you
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.
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.
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.