Optimizing Live Event ATC with Machine Learning

Even though our IFATC controllers are more than capable of managing traffic flow during busy live events, what if we could reduce the workload of our fellow controllers?

We could develop a machine learning algorithm to reduce ATC workload during busy live events in Infinite Flight using historical data on aircraft behavior, traffic patterns, and weather conditions to train an algorithm that can make intelligent decisions on how to direct aircraft in real-time.

These are just some examples and applications of machine learning solutions that can be used to reduce ATC workload in Infinite Flight during busy live events such as Fly-ins or Fly-Outs.

1. Clustering: Clustering is a machine learning technique that involves grouping data into clusters based on their similarities. In the case of ATC directing, clustering can be used to group aircraft based on their destination, speed, altitude, and other factors, allowing ATC to direct multiple aircraft at once rather than individually.

2. Decision Trees: Decision trees are a machine learning algorithm that can be used to make decisions based on a set of rules. In the case of ATC directing, decision trees can be used to determine which aircraft to direct first based on their position, speed, and other factors, reducing the workload for human controllers.

3. Neural Networks: Neural networks are a machine learning algorithm that can analyze complex patterns and relationships in data. In the case of ATC directing, neural networks can be used to analyze historical data on traffic patterns and weather conditions to predict how aircraft will behave in real-time, allowing ATC to make more informed decisions on how to direct them.

4. Reinforcement Learning: Reinforcement learning is a machine learning technique that involves training an algorithm to make decisions based on rewards and punishments. In the case of ATC directing, reinforcement learning can be used to train an algorithm to make decisions that optimize traffic flow, reducing the workload for human controllers.

5. Deep Learning: Deep learning is a machine learning technique that involves training neural networks with multiple layers to analyze complex patterns in data. In the case of ATC directing, deep learning can be used to analyze large amounts of data on aircraft behavior and traffic patterns, allowing ATC to make more informed decisions on how to direct aircraft during busy live events.

These are just a few examples of the many machine learning solutions that can be used to reduce ATC workload during busy live events in Infinite Flight. By developing and implementing these solutions, the workload of our fellow IFATC hardworking controllers can be reduced, resulting in a more efficient and enjoyable experience for players.

To add on to the machine learning solutions mentioned earlier, incorporating SIDs (Standard Instrument Departures) and STARs (Standard Terminal Arrival Routes) into the model can further enhance the accuracy and efficiency of automated ATC directing in Infinite Flight during busy live events.

SIDs and STARs are pre-defined routes that aircraft must follow when departing or arriving at an airport. By incorporating this information into the machine learning algorithm, the system can automatically direct aircraft along these routes, reducing the workload for human controllers.

For instance, here some some additional datapoints to take note of when further enhancing our model.

1. Real-Time Weather Data: Incorporating real-time weather data into the machine learning algorithm can enable the system to make more informed decisions about how to direct aircraft. For example, if there is a storm in the area, the system can automatically redirect aircraft to avoid the storm.

2. Collaborative Decision Making: Collaborative decision making involves sharing information and decision-making authority between multiple stakeholders, including human controllers, pilots, and the automated ATC directing system. By incorporating collaborative decision making into the model, the system can work more efficiently with human controllers and pilots to ensure safe and efficient traffic flow.

3. Integration with Flight Planning Software: Integrating the machine learning algorithm with flight planning software can enable the system to automatically generate flight plans for aircraft based on their destination, speed, and other factors. This can reduce the workload for pilots and improve the accuracy of automated ATC directing.

4. Adaptive Learning: Adaptive learning involves training the machine learning algorithm in real-time based on feedback from human controllers and pilots. This can enable the system to continuously improve its performance and adapt to changing conditions.

By incorporating these enhancements into the previous machine learning model, automated ATC directing in Infinite Flight during busy live events can become even more efficient, accurate, and safe.

To put it in a nutshell , basically an autopilot for ATC controllers……

There are however, ETHICAL issues with regards to the development of this ML model, since in this case people would assume that the “Computer” is taking over the controlling from our actual human controllers.

What are some of the ethical challenges or hurdles with regards to the implementation of such models within Infinite Flight ?

Any thoughts on this perhaps ?

2 Likes

I dont understand, is this a #features request? tldr tbh

2 Likes

My sincere apologies if it looks like a feature request. I just had some thoughts on reducing workload for controllers especially during live events.

1 Like

Maybe iʻd put this in the #atc category then. Also make it a bit shorter? Thats a lot to read.

Yeah I’d figure that my categorisation skills could use more work. Feel free to recategorise appropriately as I believe I don’t have the necessary abilities to do so myself given my trust level. Apologies for the lengthy thread.

No! That takes the fun out of it. Believe it or not, we enjoy controlling ;)

3 Likes

I’d agree on that one 😅. I’m quite certain that controllers love seeing pilots follow their instructions with immediate compliance and as a result you can slowly start seeing a traffic pattern form with time, which could be oddly satisfying with huge volumes of planes coming through. ;)

Yeah! It’s fun to see all of those newbie’s pilots not knowing how to use ATC instructions.

I’m with tuna to say no to this because it’s not amusing

1 Like

And do you know why the busiest airport featured of the day is practically always full open?

That’s because when more people come, it’s more amusing and there is always someone who want to take the frequency when you finish

1 Like

Glad to hear from the other end of the spectrum! I’d say it’s sort of a 2-course meal, in which the main course served is seeing newer pilots dabble aimlessly with the available ATC communication options at their disposal, with a side of violations being issued out by the exasperated controllers! I’d be guilty of that too if I was an IFATC controller 😂

I’m very kind with vios so if someone get one from me, it’s probably a big problem 😂

exemple today 2h FAOR : 0 violations

somtimes I ask myself why I’m soo kind with people even when they don’t follow, I repeat or say warning but it end really not with a vio 😝

Whilst this is an interesting idea and it’s clear that you’ve put a lot of effort and thought into this, I believe it would take away the fun for a lot of IFATC controllers.

IFATC controllers love these busy live events and the hustle and bustle of the arrivals and departures at these airports. IFATC controllers have the resources and the means to deal with these traffic levels such as splitting frequencies, requiring the use of SID/STAR’s and so forth.

Whilst you’re idea is interesting, I do believe the fun of controlling, is controlling busy events like those described above. It’s basically a gigantic game of Tetris.

7 Likes

It’s controllers like you who are empathetic and understanding enough, that are continuously drawing that solid line between AI and actual human beings, so much that it’s gonna take a long time for AI to catch up in that aspect.

Personally I don’t think this is necessary. There’s plenty of other actions controllers could take to mitigate the workload. My question is can they make a coordinated effort to do so?

1 Like

Yes, it is possible for IFATC controllers to coordinate their efforts and work together to optimize traffic flow during busy live events in Infinite Flight.
By communicating with each other, dividing workload, sequencing aircraft, creating holding patterns, and monitoring uncontrolled airports, controllers can ensure that all aircraft are safely and efficiently managed, and that the event runs smoothly.
A coordinated effort among IFATC controllers is essential to ensure the best possible experience for pilots and to maintain a high level of safety and efficiency during busy events. I’m pretty sure they have an existing private method of communication with each other during live events to ensure good coordination.

Directing multple aircraft at once given similar characteristics is counterintuituve. If you have two aircraft at the same altitude and speed the same distance from the same destination, don’t give them the same instructions. That would not separate them. In ATC, we have used “paper stops”(google if you wish) since the beginning of the industry to separate aircraft, which is quite literally the opposite of clustering.

Sequences to determine which aircraft go first don’t just depend on quantitve data; it also depends on the needs of the controllers around you, reducing undue delays, TMIs(traffic management intiatives), varying separation requirements, etc etc. Not to mention there is never a correct sequence in ATC; as long as you pick one and stick to it you’ll be fine. There is never a right or wrong answer in terms of who goes first.

What data would you be using to construct a nueral network, and what good would it do? Any historical data to be analyzed already has been(AAR/ADR for example) and is already in use.

I wouldn’t want a system to be trained based on reward and punishment when the punishment are planes crashing, not to mention there is no objective optimization of traffic, just adherence to separation requirements while not oversaturating airspace.

Again, not sure what data would be analyzed here and what that would actually accomplish besides nebuluous efficiency.

All of this is fairly vague and seems to be written by some sort of ML or AI program, so I took a quick look. Somewhat ironic.

12 Likes

Appreciate your response! Thanks for pointing that out , I initially assumed that users would know it was written by an AI given the formatting and language tones.

Again apologies for giving off the wrong intentions. I’ve mentioned my intentions in the reply above. Have a nice day 👍

1 Like

every. response, by this OP is paragraphs

1 Like

glad you enjoyed the format its presented on ! Paragraphing is common with AI generated texts, most of which contain no emotion at all unless u add specific prompting. I think this topic was a great starter for me to delve into, loved the ideas and responses made ! Thank you all for your responses.

They didn’t say that they’re enjoyed it…

1 Like