Data-Driven Conflict Resolution Generator
The current-day separation assurance is provided by air traffic controllers, but most of conflict resolution approaches are model-based (using flight dynamics)
Objective: to build a model that generates resolution methods (called Data-Driven Resolution Generator, D2RG) based on knowledge extracted and characterized from flight data which provides separation assurance
Conflict Dataset Construction
Resolution Method (Label)
Conflict Situation (Features)
Resolution Type: Hierarchical Classification
Conflict type matching: use flight phases
Upper-level classifier: maneuvering dimensions
Lower-level classifier: resolution types within specific dimension
Output: Ranked list of resolution types with corresponding likelihoods
Example: true label = Direct-to
Resolution Parameters: Optimal Parameter Design
Use analytic or data-driven approach to find resolution parameters, which guarantee separation assurance
<Horizontal/Speed>
Analytically find optimal heading/speed adjustment which can resolve conflict
<Vertical>
Use classification algorithm with the same feature set
D2RG Test Results with Simulated Flight Data
Among 54,300 flights over CONUS, there are 14,599 conflicts
Resolution Types: Accuracy 84.12%
Resolution Parameters: D2RG guarantees separation assurance, i.e. all the conflicts are safely resolved
Related Publication
K. Kim, R. Deshmukh and I. Hwang, “Development of Data-Driven Conflict Resolution Generator for En-Route Airspace,” AIAA Journal of Aerospace Information Systems, (submitted on December 10, 2017; under review)