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blogMarch 2nd 2021 • 8 m read

Important data that you are probably not using to improve your turnaround operations

It’s not rare for airports and airlines to face different issues during apron operations such as obstacles on a supposedly available stand or incorrectly defined Target Off-Block Time (TOBT), etc. These issues often lead to delays, additional spending and most importantly, safety problems.

Do airports and airlines have the power to change it?

While it’s hard to eliminate all potential issues on the apron during turnaround operations, it is possible to minimize and prevent many of them. For this, airports and airlines need to understand their bottlenecks and pain points. In this process, having data on turnaround operations is key.

In many instances, however, data on operations is not collected at all. In other cases, the data is not structured, stored or digitized and as a result, disappears.

What data do airports and airlines need to improve their turnaround operations on the apron?

1. GSE telemetry data

What is GSE telemetry data

Ground Service Equipment (GSE) telemetry data includes parameters such as speed, location, proximity of GSE to one another, operator details, operational status (on/off, idling, etc.), service proximity, mileage, fuel consumption, amount of fuel/charge left and other parameters.

How to get GSE telemetry data

Currently, GSE telemetry data is primarily collected via IoT sensors installed on each GSE. These sensors transmit data directly to a database.

Another more efficient option is to use existing cameras and computer vision to get the data about the speed, trajectory and other GSE parameters in real time. The picture below shows an example of what information computer vision and cameras can provide.

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Why you need GSE telemetry data

It is possible to have both historical and real-time GSE telemetry data. Collecting and analyzing this data provides an overview of compliance to safety standards, GSE utilization and operational status, unit effectiveness, condition of GSE fleet, quality of asset management, and service intervals.

Getting this data can help to detect which units are idling and utilize them where they are most needed instead of being left unattended. This will result in increasing time between service intervals, reducing costs, delays and CO2 emissions. In addition, GSE telemetry data allows to increase the rate of claimable damage and decrease insurance costs by making operations more transparent.

2. Centerline and docking compliance data

What is centerline and docking compliance data

During taxiing and docking, pilots should follow a centerline in order to avoid delays and collisions with other aircraft, vehicles and infrastructure. They should also stop at the right place. At the stand, marshallers or visual docking guidance systems (VDGS) assist further. They notify pilots if steering is correct, or if any adjustment is needed; they also signal when to stop.

Data on centerline and docking compliance represents how accurately the aircraft is following apron markings (e.g., deviation from a taxilane in inches/centimeters).

How to get centerline and docking compliance data

To get the data, airports and airlines can use cameras paired with artificial intelligence, which can detect how accurately the aircraft front- and main-landing gear are following the centerline. This data can be used to issue alerts in real time if any serious deviations are detected.

The picture below demonstrates an example of how cameras and artificial intelligence can be used to monitor docking compliance.

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Why you need centerline and docking compliance data

It’s important to stay centered and stop on time to avoid delays and accidents. Sometimes these accidents happen because of judgmental understeering or oversteering.

Imagine the following situation. The aircraft is parked incorrectly and as a result, it needs a pushback tug to be repositioned. The pushback tug is unavailable. Because of that, all operations on arrival and an outbound flight are going to be delayed. And although this is already bad enough, the above example is a rather lucky case. In the worst case it can lead to two aircraft clipping their wings or engines being heavily damaged.

This is why you need centerline and docking compliance data.

Gathering and analyzing such data can help to maintain safety and make sure that violations are investigated and addressed. It can also help to define patterns that lead to missing the centerline and passing the stop line.

Airports and airlines can also use historical data on centerline and docking compliance in training purposes, for example, to address judgmental understeering or oversteering.

3. Automated turnaround timestamps for all apron operations

What are automated turnaround timestamps

Automated turnaround timestamps automatically provide date, time and type of operation that is happening to an aircraft.

Examples of automated turnaround timestamps:

Screen Shot 2021-01-19 at 22.15.18.png

In addition to events such as “passenger door open”, “bridge operator left”, etc., it is also possible to timestamp events related to safety. For example, timestamps can show if chocks and cones are placed and removed, if there are people under wings and fuselage, and other safety-related events.

It is also possible to capture timestamps with real-time stand availability status (e.g., “stand clear” or “stand occupied”), display and instantly forward them to respective parties.

How to get automated timestamps

In addition to the data described above, computer vision and artificial intelligence can also help to get automated timestamps. Airports and airlines can use existing camera infrastructure to capture all events that occur during the turnaround.

When a trained software processes the timestamps, it creates alerts and serves as an assistant to those responsible for the turnaround. These timestamps can be stored in the database.

Why you need automated timestamps

Automated timestamps to improve safety

Using automated timestamps can help to improve safety. It is possible to train computer vision software to detect at-risk behavior and send notifications in case of safety violations. In many cases such notifications can prevent incidents and thus reduce injuries and damages to aircraft.

Automated timestamps also help to reveal or quantify previously unknown safety violations (e.g., no safety vests while on the apron). This data can be used in staff training purposes.

Automated timestamps to assist ramp officers

Usually ramp officers capture procedures that happen to the aircraft during the turnaround. Since they have many tasks, they might miss some events and enter details to the airport database inaccurately and later than expected, if at all. This can lead to incorrect or missing data and as a result, delays.

That is when automated timestamps can be useful – they can take care of timestamping. Ramp officers will be able to focus on other tasks and manage more turnarounds simultaneously. It will decrease operational costs for airports.

Automated timestamps to make operations more transparent

Standardizing automated turnaround timestamps can take the aviation industry to a different level of transparency and efficiency. Synchronizing turnaround timestamps with the airport operational database (AODB) provides all stakeholders with accurate and unbiased data. Also, these timestamps allow to trace SLA adherence in real time.

The chart below is an example of how operations comply with SLA based on Assaia’s ApronAI timestamps. It shows the comparison between the latest jetbridge retraction time before departure permitted by SLA (orange line) and the actual end time of jetbridge retraction (blue line). Each jetbridge retraction operation should be completed no later than 3 minutes before departure. This chart shows that there were cases when the jetbridge was retracted less than 3 minutes before departure.

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Automated timestamps to accurately predict off-block time

Turnaround timestamps can also be fed to machine learning (ML) algorithms to accurately predict off-block time. When off-block time is predicted by ML algorithms using either manually or automatically created timestamps, we call it predicted off-block time (POBT). Automated timestamps are more accurate than manually created timestamps, which significantly improves the accuracy of POBT.

The chart below shows that POBT is always more accurate than TOBT. The accuracy increases even greater shortly before departure.

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Automated timestamps were used

Ability to accurately evaluate off-block time positively influences stand allocation capacity.

Stand allocation is a dynamic process that requires a lot of last-minute changes. Critical point in ad-hoc stand allocation adjustment is departure time. It is so important because inability of the departing aircraft to leave on time corrupts the following arrival/departure sequence for that stand and may affect the entire stand allocation sequence.

Currently, TOBT serves as an industry data standard of estimated aircraft readiness time. TOBT is provided by a person who is responsible for the turnaround, it’s a rough judgmental estimate. TOBT has low accuracy and is frequently updated very late (shortly before Scheduled Off-Block Time or SOBT), which reduces decision-making power of stand planners. It also leads to delays and extended time buffers that should be reduced in order to cut operational costs and carbon footprint.

Accurate data is key to more accurate readiness estimates. Automating off-block time estimations (i.e., using POBT) will enable higher accuracy of estimating off-block time.

Automated timestamps to increase situational awareness on stand availability

It may seem that getting the data about aircraft stand availability should not be an issue: arrival and departure schedules should provide all the details. Using the schedule, however, is not enough.

Airports struggle to keep track of available and occupied stands. Apart from departure/arrival delays, sometimes GSE may be left unattended on the stand or simply be parked incorrectly. Even when the stand is scheduled to be available, it might be occupied. These obstacles delay aircraft parking and pose a safety threat.

Timestamps on stand availability can and should be used to increase situational awareness on stand availability, which in turn saves time and promotes safety of apron operations.

The picture below demonstrates an example of timestamping “stand not clear” event:

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Use data to improve apron operations

On the surface, it may seem that airports and airlines already have a clear enough picture of their apron operations. Some information, however, might be hidden from the view, which prevents them from achieving greater success.

Therefore, as discussed in this blog post, airports and airlines need to get:

  • - GSE telemetry data
  • - Centerline and docking compliance data
  • - Automated turnaround timestamps for all apron operations

Airports and airlines that know how to get and use this data will have more control over operations, more power to make better decisions for positive changes, and as a result, become more effective, competitive, reduce carbon footprint and save money.

Petr Zhigalin has over 6 years of experience in aviation. He worked as a business aviation supervisor and as an airline duty manager. He has expertise in both ground handling and terminal operations. His current role includes analysis of aviation-related data generated by artificial intelligence.