Congestion at major airports worldwide results in increased taxi times, fuel burn, and emissions. Regulating the pushback of aircraft from their gates, also known as departure metering, is a.

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Airports are busy places where different stakeholders have key roles and a common goal to manage safely and efficiently the flow of flights departing and arriving. However, airport infrastructures are not exploited in the most optimal manner and increasing traffic makes it difficult for operations to be proactive rather than reactive. This is due to the lack of good information sharing procedures, each of the stakeholders involved in operations has a piece of the information rather than the global picture.

With a 3 hour look-ahead, the airport collaborative decision making (A-CDM) process enables all stakeholders to benefit from sharing the same information as early as possible in order to take informed decisions. Milestones and relevant flight details are updated and shared in order to have accurate Off-Block Times (OBT) and Take-Off Times (TOT) for better situational awareness. This information sharing leads to better traffic flow management at the network level.

Look at other dictionaries: Time Warner Cable Arena — Former names Charlotte Bobcats Arena (2005–2007) Location Wikipedia. Lackawanna Cut-Off — The westbound Lackawanna Limited comes off the Pequest Fill shortly after the opening of the Lackawanna Cut Off. AOBT Definition. The time the aircraft pushes back / vacates the parking position. Definition Source. EUROCONTROL/IATA/ACI, Airport CDM Implementation Manual, V.4, March 2012. VATSIM is completely free to join and use, including all of our pilot and ATC software that allows you to connect to our global network. Whether you want to join us as a virtual Pilot or virtual Air Traffic Controller, the initial registration process is the same.

In this article, we will explain what is the Target Off-Block Time (TOBT) and how it is used in the A-CDM concept. We will also focus on why its accurate estimation is of great value for the airport operations. After that we will present the current capacity of Innov’ATM Airport dedicated product AirportKeeper in predicting TOBT in order to prevent disruption due to the turnaround process management. Finally we will explain how we explored the use of Machine Learning algorithm in order to enhance the TOBT prediction and what are the results on the prediction accuracy.

2.1 – What is the TOBT used for

TOBT is the target time set as off-block departure time by the airline or ground handler in charge. At this time:

• airplane’s doors are closed

• boarding bridge is removed

• push back vehicle is available

• airplane is ready to start up/push back upon clearance.

TOBT and its updates improve predictability during the turnaroud process of aircraft.

• It contributes to better allocation of parking stand ressources through knowledge of parking stand occupancy time and aircraft stand allocation accuracy.

• It contributes to better workload management of ATC controllers that can anticipate the number of flights that will use the platform simultaneously.

• By using Variable Taxi Times, a proper prediction of take-off times can then be communicated to the Network Manager Operation Center as an input for the management of European network.

This TOBT is the base time used by the Air Traffic Controllers to organize the sequence of aircraft to take-off and deliver push back clearance as described in paragraph 1.1.1 & 1.1.2 of our previous article Reducing operational costs with Innov’ATM / AI – Episode 1: Ground holding time… This is why its accuracy and stability is important in order to provide an accurate and stable flight departure sequence.

2.2 – What is the TOBT life-cycle

The TOBT is declared manually by the airline or the ground handler in charge. It is well observed that the TOBT is updated very lately in the TOBT lifecycle. This is due to various reasons among which:

• Ground handlers are busy trying to get the A/C ready for push back on schedule.

• Parking fees are proportional to stand occupation time, so ground handlers job is to try to minimize this time (it has to be noted that ground handlers get penalties when the delay can be blamed on them).

• Ground handlers do not want to loose their place in the departure sequence, which would result in a bigger delay (when TOBT changes, some rules are applied by ATC that may result in removing the flight from the departure sequence and rescheduling it later)

• Some events are independent from ground handlers will, and they have no control on it (boarding longer than usual, passenger no show, …)

As a consequence, TOBT are updated only when the problem is confirmed, and when no other option is possible, which means updates happen very lately in the turnaround lifecycle. This is a pain point in A-CDM Information Sharing objective. It then becomes crucial to be able to predict the TOBT that will be issued in order to increase Information Sharing among all stakeholders and optimize global efficiency. We will refer to the prediction of target off-block time as the POBT in this article.

The TOBT prediction (i.e POBT) allows operators to identify potential disruptions between what is currently the shared target of off-block times and the actual capacity of the aircraft to be ready for off-block regarding its current status. Today Innov’ATM AirportKeeper product already integrate a POBT computation which is displayed side to the TOBT value in its information sharing flight list.

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This POBT is for example used to raise an alert indicating that the TOBT shall be updated by the ground handler when POBT > TOBT + 5 min.

The current POBT computation algorithm we use in operation is a simple and logical business rule based algorithm that relies on flight informations available along the life cycle of a turnaround flight. We will name the POBT computed by this algorithm POBT(R) in the rest of this article.

Based on the current status of the flight, this algorithm combines the most accurate informations in the following values:

• SOBT – The scheduled off-block time of the flight.

• EOBT – The estimated off-block time of the flight.

• CTOT – The calculated take of time of the flight.

• O-ATOT – The actual take of time of the flight at its origin airport.

• ELDT – The estimated lading time of the flight.

• EXIT – The estimated taxi-in time of the flight.

• MTT – The minimum turnaround time of the flight.

• EXOT – The estimated taxi-out time of the flight.

• ALDT – The actual landing time of the flight.

• AIBT – The actual in-block time of the flight.

As an example, as soon as the aircraft land at the airport, the POBT(R) is computed by combining the Actual Landing Time (ALDT) with the Estimated Taxi In Block Time (EXIT) and the Minimum Turnaround Time (MTT) of the flight:

The current accuracy of POBT(R) compared to the final value of TOBT has been evaluated on a month of data at one of the airport where AirportKeeper is deployed. The evaluation has been performed in regards to the prediction horizon, i.e. the time before flight scheduled departure (SOBT) at which we would like to predict the final value of TOBT. The prediction horizons used are 3 hours, 2 hours, 1 hour, 40 mins, 30 mins, 20 mins and 10 mins before SOBT.

First of all the distribution of the average error shows that most of the predictions are within a +/-5 minutes ranges.

We then computed the mean absolute error (MAE) of POBT(R) vs the final TOBT. The MAE is an arithmetic average of the absolute difference between predicted and actual values.

It is clearly seen how the prediction error decreases with the decrease of prediction horizon, from 11 minutes 3h before the flight departure to 8.5 minutes 10 minutes before the flight departure.

Even if this prediction is already usable in operation, we decided to give a try to Machine Learning (ML) algorithm to see if we could get a more precise prediction for POBT computation.

Artificial Intelligence (AI) is the ability of a digital computer or computer-controlled robot to perform tasks commonly associated with intelligent beings. The aim of AI is to improve computer functions which are related to human knowledge, for example, reasoning, learning, and problem-solving. ML is a branch of artificial intelligence that can be defined as the study of computer algorithms that allow computer programs to automatically improve through experience.

If you are interested in getting more information on the concept of ML and how it is applied, you can consult paragraph 2.1 and 2.2 our previous article Reducing operational costs with Innov’ATM / AI – Episode 1: Ground holding time.

The POBT computed thanks to ML will be named POBT(ML) in the rest of this article.

In the ideal word, the best situation that could occur is when all flights respect their schedule precisely, i.e. TOBT= SOBT for all flights. In reality that is evidently not possible, and delay may be introduced to the schedule. This delay is computed as :

Our aim is to predict more accurately this TOBT_delay, i.e. predict for how much a flight would divert from its schedule off-block time. Thanks to this TOBT_delay prediction we will be able to predict the TOBT related to the flight SOBT, and thus compute a POBT as :

After validation and cleaning of the historical data available to train our ML model, the result flight set contains 8249 flights.

Following features derived from flight data were used:

• day: day of week (derived from SOBT)

• hour: hour of day (derived from SOBT)

• nbScheduled: number of flights scheduled to depart within the hour of SOBT

• acTypeIcao: aircraft type

• airlineIcao: operating airline

• departureParking: flight parking

• adesGroup: destination zone (retrieved from first letter of ADES code)

• EOBTdiff: estimated delay at prediction horizon, i.e. difference between last EOBT received by the prediction time and SOBT, in minutes

• TOBTdiff: expected delay at prediction horizon, i.e. difference between last TOBT received by the prediction time and SOBT, in minutes (see to Figure below)

• numTOBT: number of TOBT updates received up to the prediction horizon

• hasPrevious: boolean, 1 if flight has preceding arrival flight, 0 otherwise

• arrivalMilestone: milestone of preceding arrival flight (if any) at prediction horizon

• adepGroup: departure zone of preceding arrival flight (retrieved from first letter of ADEP of preceding arrival flight if any)

• SIBTdiff: difference between SIBT of preceding arrival flight (if any) and SOBT, in minutes

• arrDelay: expected or actual delay of preceding arrival flight (if any) at prediction horizon, i.e. difference between most relevant IBT of preceding arrival flight (in order, AIBT, TIBT, EIBT) received by the prediction time and its SIBT, in minutes.

As for the POBT(R) algorithm, we evaluated the distribution of prediction errors at different prediction horizons (3h to 10 min before SOBT).

As can be seen, the distribution is well-centered at 0 and the majority of flights have the prediction error in the range of +/-5 minutes. It is also seen that the number of flights having smaller errors is increased with the decrease of prediction horizon.

We then computed the MAE for different horizons, and compared the results with the previous POBT(R) algorithm.

The POBT(ML) is slightly more accurate than the POBT(R) that is currently used in our AirportKeeper product.

POBT computation by ML algorithm (i.e POBT(ML)) provides very good results. Using features mostly related to flight data, we were able to enhance significatively the accuracy of prediction in comparison to the Off-Block Time computed based on business logic (i.e POBT(R)).

Thus, the ML-based Off-Block Time computation is an option integrable in our AirportKeeper product. When in operation, more data related to actual airport (meteo, pax count, boarding progress status…) will allow to train more and more the algorithm and provide a very good level of Off-Block Time prediction.

Getting Started

To test beta versions of apps and App Clips using TestFlight, you’ll need to accept an email or public link invitation from the developer and have a device that you can use to test.

Members of the developer’s team can be given access to all builds of the app.

All other invited testers can access builds that the developer makes available to them. A developer can invite you to test with an email or a public link.

Required platforms

• iOS apps: iPhone, iPad, or iPod touch running iOS 8 or later. App Clips require iOS 14 or later. iMessage apps and sticker packs require iOS 10 or later.
• tvOS apps: Apple TV running tvOS 9 or later.
• watchOS apps: Apple Watch running watchOS 2 or later.

TestFlight is not available for Mac apps.

Available Languages

TestFlight for both iOS and tvOS is available in Arabic, Catalan, Chinese (simplified), Chinese (traditional), Croatian, Czech, Danish, Dutch, English (Australia), English (U.K.), English (U.S.), Finnish, French, French (Canada), German, Greek, Hebrew, Hindi, Hungarian, Indonesian, Italian, Japanese, Korean, Malaysian, Norwegian, Polish, Portuguese (Brazil), Portuguese (Portugal), Romanian, Russian, Slovak, Spanish, Spanish (Latin America), Swedish, Thai, Turkish, Ukrainian, and Vietnamese.

Installing and Testing Beta Apps

Each build is available to test for up to 90 days, starting from the day the developer uploads their build. You can see how many days you have left for testing under the app name in TestFlight. TestFlight will notify you each time a new build is available and will include instructions on what you need to test. Alternatively, with TestFlight 3 or later, you can turn on automatic updates to have the latest beta builds install automatically.

When the testing period is over, you'll no longer be able to open the beta build. To install the App Store version of the app, download or purchase the app from the App Store. In-app purchases are free only during beta testing, and any in-app purchases made during testing will not carry over to App Store versions.

Installation

To get started, install TestFlight on the device you’ll use for testing. Then, accept your email invitation or follow the public link invitation to install the beta app. You can install the beta app on up to 30 devices.

Installing a Beta iOS App via Email or Public Link Invitation

1. Install TestFlight on the iOS device that you’ll use for testing.
2. Open your invitation email or tap on the public link on your iOS device.
3. Tap View in TestFlight or Start Testing; or tap Install or Update for the app you want to test.

Installing a Beta tvOS App via Email Invitation

1. Install TestFlight on Apple TV.
2. Open your invitation email on a mobile device or computer.
3. Click or tap Start Testing. You'll be taken to a web page with a redemption code.
4. Open TestFlight on Apple TV.
5. Go to Redeem and enter the redemption code.

Installing a Beta tvOS App via Public Link Invitation

1. Install TestFlight on an iOS device and Apple TV where you can sign in to the same App Store account.
2. Tap the public link on your iOS device.
3. Tap Accept for the app you want to test.
4. Open TestFlight on Apple TV. You must be signed in to the same App Store account you used on your iOS device.
5. Install the app you want to test.

Installing a Beta watchOS App via Email or Public Link Invitation

1. Install TestFlight on the iOS device that you’ll use for testing.
2. Open your invitation email or tap on the public link on your iOS device.
3. Tap View in TestFlight or Start Testing.
4. If you're testing an app that’s for Apple Watch only, tap Install or Update from the Apps list.
5. If the app is an iOS app that includes an Apple Watch app, install the iOS app first, then from the App Details page under the Information section, you will see a Watch section. If the Apple Watch app is available and compatible with your watch, you’ll see a button to install it.

Testing

Testing iMessage Apps (iOS 10 or later)

1. Install TestFlight on the iOS device that you’ll use for testing.
2. Open your invitation email or tap on the public link on your iOS device.
3. Tap View in TestFlight or Start Testing; or tap Install or Update for the app you want to test.
4. If you’re testing an iOS app that includes an iMessage app, launch the beta app from the home screen as you would with any app.
5. If you’re testing an app that’s for iMessage only or a sticker pack, you can launch it from inside Messages.

Testing Beta App Clips (iOS 14 or later)

After accepting your email or public link invitation to test the app, you’ll see the option to test the App Clip in TestFlight. You can install either the app or the App Clip on your device (but not both at once), and can replace one with the other at any time. If the app is installed on your device, testing the App Clip will replace the app and some app data may be lost. You can reinstall the app by tapping Install on the app’s page in TestFlight.

1. Install TestFlight on the iOS device that you’ll use for testing.
2. Open your invitation email or tap on the public link on your iOS device.
3. Tap View in TestFlight or Start Testing; or tap Install or Update for the app you want to test.
4. Go to the app’s page in TestFlight.
5. In the App Clips section, tap TEST next to the beta App Clip you want to test.

Managing Automatic Updates

After installing TestFlight 3 or later, you’ll be prompted to turn on automatic updates. This allows the latest available beta builds to install automatically. TestFlight will notify you each time a new build is installed on your device. Automatic updates can be turned off at any time.

Change automatic update settings for all of the beta apps you’re testing using TestFlight:

TestFlight for iOS

1. Open TestFlight and tap Settings in the upper-right corner.
2. Tap Automatic Updates.
3. Tap On or Off.

TestFlight for tvOS

1. Open TestFlight and click the Settings tab at the top.
2. Under GENERAL INFORMATION, turn Automatic Updates On or Off.

Change automatic update settings for individual beta apps you’re testing using TestFlight:

TestFlight for iOS

1. Open TestFlight and go to the app’s page.
2. Under App Information, turn Automatic Updates On or Off.

TestFlight for tvOS

1. Open TestFlight and go to the app’s page.
2. Under the app icon, click the More button.
3. Click Turn On Automatic Updates or Turn Off Automatic Updates.

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Testing Previous Builds

When viewing an app in TestFlight, you'll see the latest available build by default. You can still test all other builds that are available to you.

1. Go to the app’s page in TestFlight.
2. Tap on Previous Builds.
3. Tap and install the build you want to test. The one you select will replace what’s currently installed.

If you already have the App Store version of the app installed on your device, the beta version of the app will replace it. After you download the beta app, you’ll see an orange dot next to its name that identifies it as a beta.

When you accept a TestFlight invitation through a public link, your name and email address are not visible to the developer. However, they’ll be able to see your number of sessions and crashes, the day you installed their app, and the latest installed version.

Giving Feedback

While testing a beta version of an app or App Clip, you can send the developer feedback about issues you experience or make suggestions for improvements based on the “What to Test” content. Feedback you submit through TestFlight is also provided to Apple as part of the TestFlight service.

iOS Apps

If your device is running iOS 13 or later, you can send feedback through the TestFlight app or directly from the beta app or beta App Clip by taking a screenshot, and you can report a crash after it occurs. If you were invited to test an app with a public link, you can choose not to provide your email address or other personal information to the developer. Apple will also receive all feedback you submit and will be able to tie it to your Apple ID.

Sending Feedback through the TestFlight App (iOS 13 or later)

1. Open the TestFlight app on your device.
2. From the Apps list, tap the app.
3. Tap Send Beta Feedback.
4. In the share dialog, tap Include Screenshot to choose a screenshot. If you don’t want to send an attachment, tap Don't Include Screenshot.
5. Add your comments (up to 2,000 characters), and optionally enter your email address if you were invited with a public link.
6. Tap Submit.

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Sending Feedback through the Beta App (iOS 13 or later)

When you take a screenshot while testing a beta app or beta App Clip, you can send the screenshot with feedback directly to the developer without leaving the app or App Clip Experience. Developers can opt out of receiving this type of feedback, so this option is only available if the developer has it enabled.

1. Take a screenshot on your device. For details on how to take screenshots, see Take a screenshot on your iPhone, Take a screenshot on your iPad, and Take a screenshot on your iPod touch.
2. A thumbnail of your screenshot appears in the lower-left corner of your device. Tap the thumbnail and, if needed, add drawings and text with Markup. Then tap the Done button.
3. Tap the Share Beta Feedback.
4. Optionally, you can add comments (up to 2,000 characters), and your email address if you were invited with a public link.
5. Tap Submit.

Sending Crash Information (iOS 13 or later)

If you experience a crash while testing a beta app or beta App Clip, you’ll receive an alert asking if you want to send crash details to the developer through TestFlight. Developers can opt out of receiving this type of feedback, so this option is only available if the developer has it enabled.

When the crash alert displays, tap Share, add any additional comments, and tap Submit.

Sending Feedback through the TestFlight App (iOS 12.4 or earlier)

If your device is running iOS 12.4 or earlier, tap Send Beta Feedback to compose an email to the developer. The feedback email contains detailed information about the beta app and about your iOS device. You can also provide additional information, such as necessary screenshots and steps required to reproduce any issues. Your email address will be visible to the developer when you send email feedback through the TestFlight app even if you were invited through a public link.

Contacting the Developer

If you need to contact the developer while you’re testing their beta app for reasons other than feedback, you can view their email address. In TestFlight, go to the app’s page, go to the Information section, and tap App Details to view the developer’s email address.

tvOS Apps

To provide feedback on a tvOS app, open TestFlight, go to app’s page, go to the Information section to view the developer's email address, and send them an email. Provide as much information as you can, including screenshots and steps required to reproduce any issues you encountered. Please note that your email address will be visible to the developer when you send email feedback through TestFlight.

Opting Out from Testing

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If you do not accept your email invitation, the beta app will not be installed and you will not be listed as a tester, and Apple will not take any action with respect to your email address. Additionally, you can unsubscribe using the link at the bottom of the invitation email to notify the developer that you’d like to be removed from their list. If you accepted the invitation and no longer wish to test the app, you can delete yourself as a tester in the app’s Information page in TestFlight by tapping Stop Testing.

Your Privacy and Data

When you test beta apps and beta App Clips with TestFlight, Apple will collect and send crash logs, your personal information such as name and email address, usage information, and any feedback you submit to the developer. Information that is emailed to the developer directly is not shared with Apple. The developer is permitted to use this information only to improve their App and is not permitted to share it with a third party. Apple may use this information to improve the TestFlight app.

Apple retains TestFlight data for one year. To view and manage your data with Apple, including your data that is sent to Apple through TestFlight, visit Data and Privacy. For more information about how the developer handles your data, consult their privacy policy. To request access to or deletion of your TestFlight data, you should contact the developer directly.

Information Shared by Using TestFlight

The following data is collected by Apple and shared with the developer when you use TestFlight. If you accepted an invitation through a public link only, your email address and name are not visible to the developer.

Data Shared When Sending Feedback (iOS only)

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When you send feedback through TestFlight or send crashes or screenshots from the beta app, the following additional information is shared. If your device runs iOS 12.4 or earlier, this information is only shared with the developer. If your device runs iOS 13 or later, this information is collected by Apple and shared with developers. Apple retains the data for one year.

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