Data for decarbonisation transparency
Aviation is a complex ecosystem where multiple stakeholders will all need to ensure they are playing their part to ensure our planet is protected. Meaningful data to enable transparency and collaborative planning is a step towards achieving this.
This month at COP27 in Egypt, UN Climate Change in collaboration with a range of stakeholders has held a series of events focused on “Together 4 Transparency” noting that transparency helps build mutual trust and accountability and encourages countries to increase their climate ambition over time and track global progress toward the Paris Agreement goals. It also gives stakeholders like governments the information they need to track the implementation of national climate action plans (NDCs) and empowers them to make more informed decisions, set meaningful targets, and attract financial, technological and capacity-building support, particularly for developing countries.
Aviation has an opportunity on many fronts to improve the status quo, from sustainable aviation fuels to carbon offsetting to leveraging data to ensure all emission volumes and sources are understood, measured and ultimately, reduced.
Data, freely available, yet not historically accessible holds an important key to meaningful reductions and transparency. Assaia has worked with a number of airports and airlines to surface this data from events on the apron and enable them to make changes to reduce emissions. Surfacing this data enables the much-needed transparency to enable all stakeholders to achieve their sustainability goals.
Less queuing means fewer emissions
One area where real-time turnaround data can help airports to achieve their sustainability targets is by reducing aircraft holding / queueing times. For the first case, we will refer to the results Assaia has achieved together with Seattle Tacoma International Airport. At this airport, during peak hours, there are often arriving aircraft holding for their target gate to become available. The holding occurs either as a result of early arrivals or (more commonly) delays of departing flights. Previously, it often occurred that the target gate of a holding aircraft would become available but the ground controllers would not realise this. Therefore, we now have a situation where an aircraft is holding while its target gate is actually already available.
Through the use of Assaia’s computer vision system, the moment that the aircraft leaves the gate is registered and the ground controller is notified that the next aircraft should be cleared for taxi-in. As a result of this automated workflow we have reduced excess holding times at Seattle Airport by almost 80% and airlines’ kerosene costs have decreased by roughly $1 million per year. Co2 emissions have been reduced by 1.5 million kg per year.
Improved predictions
There is a second, even more, significant case related to aircraft holding times. Before we dive into this use case, it is good to explain how we use real-time turnaround data to predict aircraft off-block times. Again, AI technology is used in order to create an algorithm which continuously predicts for each aircraft when it is going to depart. Assaia’s Predicted Off-Block Time (POBT) algorithm has been validated by an independent third-party research institute to be much more accurate than current-day alternatives. The POBT can be used to clear a holding aircraft for taxi-in before the gate is even available.
For example, if the average taxi-in time is 9 minutes and the POBT for the target gate shows that there are 9 minutes remaining before the departing aircraft clears the gate, the next aircraft can be cleared for taxi-in. The arriving aircraft will then arrive at the gate just as the other aircraft pushes back. This means that the average aircraft holding time will be reduced with the average taxi-in time (9 minutes in our example). The effect of this use case on an airport like Seattle would be fivefold of the previous case.
APU monitoring
A last interesting application of real-time turnaround data for sustainability objectives is related to minimising the use of aircraft Auxiliary Power Units (APUs). The APU is a generator, typically located in the tail of an aircraft and used to generate electricity and excess CO2 emissions. A computer vision system can detect if the ground power and pre-conditioned air have been connected. Therefore, if this has not happened the system can alert the ground handler or airline in order to get it connected as soon as possible. On the departure end, the POBT can again help ground handlers to determine the ideal moment to disconnect ground power and pre-conditioned air. With one of our customers, we have realised an average increase in ground power connection time (thus time that the APU did not have to be on) of four minutes per flight. This saves 22kg of Co2 emissions per flight while it also saves the airlines money. 22kg might not sound like much but for a busy airport, it accumulates into significant amounts over the course of a year.
Aviation is a complex ecosystem where multiple stakeholders will all need to ensure they are playing their part to ensure our planet is protected. Meaningful data to enable transparency and collaborative planning is a step towards achieving this.
