ATC Sector Complexity Prediction for Dynamic Controller Allocation
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Solution Overview
Problem
Existing air-traffic-control systems face challenges in efficiently and dynamically assigning controllers to sectors due to the complexity of traffic processing, which current analytical functions struggle to accurately assess in real-time, leading to inefficiencies and high computational costs.
Innovation Solution
A computer-implemented method using supervised machine-learning engines trained with historical data and reinforcement learning to predict ATC complexity, incorporating air traffic and meteorological data, and controller workload feedback, allowing dynamic sector redefinition and controller allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If analytical functions are used to evaluate ATC sector complexity, then an ATC-complexity index can be computed based on multiple indicators, but the execution time becomes very long for particularly large sectors
Solution Approach 1:
The patent pre-computes and stores complexity indices for various sector configurations using analytical functions during an offline phase. During online operation, the system retrieves pre-computed results based on current traffic patterns rather than performing real-time analytical computations, thus eliminating long computation times while maintaining accurate complexity assessment
Solution Approach 2:
The system performs preliminary clustering of air traffic data and pre-identifies potential conflict scenarios offline. This allows the online system to work with simplified, pre-processed data structures, significantly reducing the computational burden during real-time sector complexity evaluation
2Reliability
If a high number of controllers are permanently kept in reserve to handle increases in processing complexity, then sector complexity can be managed, but this becomes inefficient and expensive
Solution Approach 1:
The patent implements dynamic sector merging and splitting capabilities that automatically adjust sector configurations based on real-time traffic complexity. When traffic volume increases, the system dynamically creates additional sectors or redistributes traffic to available controllers, eliminating the need for permanently reserved controllers while maintaining reliable complexity management
Solution Approach 2:
The system changes sector parameters (boundaries, controller assignments, complexity thresholds) dynamically based on real-time conditions. This allows the ATC system to adapt to varying traffic loads by adjusting sector configurations rather than maintaining fixed, over-provisioned controller assignments
3Ease of operation
If manual assignment of controllers to sectors is performed, then regulatory constraints can be considered, but it is not always easy to arrange for adequate controller availability when complexity increases
Solution Approach 1:
The system continuously monitors sector complexity metrics and controller workload, using this feedback to automatically adjust sector assignments and trigger alerts when regulatory thresholds are approached. This closed-loop feedback mechanism maintains regulatory compliance while enabling dynamic adaptation to changing traffic conditions
Data Source
AI summary
A computation of the processing complexity of an air-traffic-control situation is provided. In particular, the processing complexity of an air-traffic-control situation is computed using a supervised learning engine, trained using the result of analytical functions for computing the processing complexity of air-traffic-control situations.