Airport Graph Prioritization for Scalable Traffic Flow Modeling
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Solution Overview
Problem
Current air traffic flow modeling systems are inaccurate when scaling from single airports to worldwide networks, as they prioritize airports based on airport-level statistics that may not reflect their importance to network-level traffic flow, and resource-intensive machine learning models are impractical for large networks.
Innovation Solution
A directed graph-based approach that utilizes historical flight data to generate a graph with nodes representing airports and edges representing flights, applying link analysis and clustering algorithms to identify high-priority airports for modeling, which are then displayed through a GUI for accurate traffic flow prediction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning models are used to model air traffic flow for each airport, then prediction accuracy is improved, but resource consumption and system complexity increase significantly making scaling impractical
Solution Approach 1:
The patent segments the air traffic network into hierarchical levels (network-level and airport-level) and applies different modeling approaches to each. At the network-level, a simplified graph-based model is used to identify priority airports, while airport-level detailed modeling is only applied to the identified priority airports. This segmentation allows accurate predictions for critical airports without the resource burden of modeling all airports with equal detail.
Solution Approach 2:
Instead of applying full machine learning models to all airports (excessive action), the patent applies partial action by using simplified graph-based prioritization for the entire network and reserving resource-intensive ML models only for the subset of priority airports identified through the graph analysis. This partial application of complex models achieves network-level prediction accuracy with reduced computational resources.
2Adaptability or versatility
If machine learning models are trained for multiple airports to provide network-level predictions, then prediction coverage is improved, but computational resources and training time become prohibitive
Solution Approach 1:
The patent performs preliminary action by conducting graph-based prioritization analysis before applying machine learning models. The system pre-identifies priority airports using the directed graph and betweenness centrality metrics, then only trains ML models for these pre-selected airports. This preliminary filtering step reduces the number of airports requiring ML training, significantly cutting training time while maintaining network-level prediction coverage through the hierarchical approach.
3Measurement precision
If airports are prioritized based on airport-level statistics, then modeling focus is improved, but network-level importance is not accurately reflected
Solution Approach 1:
The patent transitions from airport-level statistics (single-dimension analysis) to network-level graph analysis (multi-dimensional analysis). By representing airports as nodes in a directed graph and flights as edges, the system captures the topological relationships and connectivity patterns across the entire network. The betweenness centrality metric operates on this graph structure to identify airports that are critical for network-level traffic flow, providing prioritization that reflects both local and global importance.
Data Source
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AI summary
A device includes a memory and one or more processors coupled to the memory. The processor(s) are configured to obtain historical flight data that represents a plurality of flights associated with a plurality of airports and to generate a directed graph, based on the historical flight data, that includes a plurality of nodes and a plurality of edges connecting pairs of nodes. The plurality of nodes correspond to the plurality of airports and the plurality of edges correspond to the plurality of flights. The processor(s) are configured to perform a ranking operation on the directed graph to identify one or more target nodes of the plurality of nodes. The one or more target nodes correspond to one or more airports of the plurality of airports. The processor(s) are configured to output a graphical user interface that indicates the one or more airports as recommended airports for modeling predicted traffic flow.