Air Traffic Controller Call-Load Prediction From Flight Trajectories
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for evaluating air traffic controller workload fail to accurately account for the influence of airspace structure characteristics, traffic flow density, and other factors, leading to potential security risks due to heavy ground-air call loads and inadequate time for context awareness.
Innovation Solution
A method and device for predicting call load using air traffic control data, including real-time and historical data, to analyze flight path prediction, command schemes, and calculate call time and interval times, utilizing LSTM-based models for accurate flight path and call content prediction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional workload measurement methods are used, then the evaluation process is simple, but the accuracy of workload assessment is insufficient
Solution Approach 1:
The patent segments the workload measurement into multiple independent components: flight path prediction module, command scheme identification module, call time calculation module, and call load prediction module. Each module processes specific data and performs a dedicated function, allowing the system to achieve high measurement precision through modular architecture while maintaining manageable complexity through functional decomposition.
Solution Approach 2:
The patent performs preliminary actions by predicting flight paths and identifying command schemes in advance before actual controller calls occur. The system pre-calculates call times and predicts call loads based on historical data and pattern recognition, enabling accurate workload assessment before the actual control events happen, thus improving measurement precision without requiring complex real-time processing during critical moments.
2Measurement precision
If more factors are considered in workload measurement, then the accuracy improves, but the calculation complexity increases
Solution Approach 1:
The patent divides the complex measurement process into separate functional modules: flight path prediction, command scheme identification, call time calculation, and call load prediction. Each module handles specific factors independently, allowing the system to incorporate multiple measurement factors (flight path, traffic density, airspace structure) without creating an unmanageable monolithic complexity.
Solution Approach 2:
The patent utilizes historical data and pattern recognition to create feedback loops where predicted call loads are compared with actual controller behavior patterns. The system continuously refines its predictions by learning from historical flight data and adjusting its models, thereby improving measurement precision while managing complexity through iterative optimization rather than requiring overly complex one-time calculations.
3Speed
If real-time data processing is performed, then the responsiveness improves, but the computational resources required increase
Solution Approach 1:
The patent performs preliminary computations by predicting flight paths and identifying command schemes before actual controller calls occur. The system pre-processes historical data and establishes prediction models in advance, allowing real-time call load predictions to be made using simpler, faster calculations based on pre-computed patterns rather than performing complex real-time analysis during critical moments.
Solution Approach 2:
The patent segments the computational tasks into separate modules that can process data independently and in parallel. The flight path prediction, command scheme identification, and call time calculation modules operate separately, allowing the system to distribute computational workload and reduce overall resource consumption while maintaining fast real-time prediction responses.
Data Source
AI summary
According to a method for predicting a call load of a controller, a flight trajectory of an aircraft is calculated by analyzing route information of an area to be predicted, a command intention of the controller and a flight intention of a pilot, and then a more accurate flight trajectory of the aircraft is predicted through the calculated flight trajectory, so that a future call node and content are acquired. The predicted call content is combined with the current specific control scene to predict call time required by the call content. Finally, the call load is calculated by the required call content and time and the call node, finally the purpose of predicting the call load of the controller in a time period of the future is achieved, and more reliable support is provided for timing requirements in an air traffic control process.