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

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)