Common Route Planning Mistakes in Infinite Flight Simulator

Effective route planning is crucial for a successful flight.

However, even experienced pilots can make mistakes that can lead to navigational errors, insufficient fuel, or even unnecessary violations incurred during flight. In this discussion thread, let’s share some of the common mistakes that pilots make when planning their routes in Infinite Flight Simulator, and discuss how these can be avoided.

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They enable VNAV, fall asleep and then expect their overspeed violations to be removed because the plane descended but they were asleep…

Tip: Never turn on VNAV until you are active at your device for descent and landing.

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I concur indeed. Especially during the long hauls :(

Westbound transatlantic flights using NATs and getting pounded by headwinds

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Absolutely! I agree that headwind pounding in Infinite Flight can be quite a challenge. It’s amazing how realistic the game’s simulation of wind effects on the aircraft is, especially when you’re flying into a strong headwind. It can feel like you’re battling against the elements and requires constant adjustments to maintain your course.

Your profile showed me that you wrote a thesis about machine learning and AI.

Have you considered using the Infinite Flight API to create a program that considers all the common mistakes in flight planning and creates the “perfect” flight plan for a user in Infinite Flight?

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@A320_Flyerboy19 Definitely! However, cost is always a factor in these type of developments. Assuming we have an infinite budget for additional further infinite flight development using the Infinite Flight’s API,

We can indeed create a program that considers all the common mistakes in flight planning and creates the “perfect” flight plan for a user in Infinite Flight using machine learning and AI

Here is an outline of the process in the development process for the program :

  1. Collect data: The first step in creating an AI-powered flight planning program is to collect data on common mistakes made by users. This can be done by analyzing user behavior on the Infinite Flight app and identifying patterns in their flight planning.

  2. Define the problem: Once data has been collected, the next step is to define the problem that the program will solve. In this case, the problem is to create a flight plan that avoids common mistakes made by users.

  3. Choose the algorithm: There are several machine learning algorithms that can be used to solve this problem, including decision trees, random forests, and neural networks. The choice of algorithm will depend on the specific requirements of the program.

  4. Train the model: Once the algorithm has been chosen, the next step is to train the model using the data collected in step 1. This involves feeding the algorithm with data and allowing it to learn patterns and relationships within the data.

  5. Test the model: After the model has been trained, it needs to be tested to ensure that it can accurately identify common mistakes made by users and create a flight plan that avoids them.

  6. Deploy the model: Once the model has been tested and validated, it can be deployed as a tool for users of the Infinite Flight app. The program should be integrated with the app in a way that allows users to input their flight information and receive a flight plan that takes into account common mistakes.

  7. Continuously improve the model: Finally, the program should be continuously updated and improved to ensure that it remains effective in avoiding common mistakes made by users.

Overall, creating an AI-powered flight planning program for Infinite Flight requires a combination of data analysis, machine learning, and software development. With the right approach, it is possible to create a program that can help users avoid common mistakes and enjoy a smoother, more efficient flying experience.

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Use Simbrief or Infinite Flight FPL Converte to properly prepare your flight, it will help you with Block Fuel to be loaded on board, V speeds, Route Planning ( All customizable such as avoiding a specific airspace)

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Well, of course, we could use those too. But developing an AI to build you the “perfect” flight plan in a matter of seconds seems to me a better option.

You could also have the option of taking data from those websites or seeing if there is an API to where you can also integrate that to be a perfect FPL.

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You could use Skyvector for North American flights.

It features fastest routes (green leaf), favorite routes (heart), and ATC preferred routes (Blue Gavel).

I use it for almost every flight I execute now.

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