Extracting aircraft conflict-resolution situations from historical ADS-B data

Résumé fourni par la source

Existing conflict resolution models are often based on theoretical frameworks that, while providing optimal solutions under specific criteria, may not fully align with real-world controller decision-making practices. This gap between model predictions and actual behaviour can lead to low acceptance of automated tools. Understanding how controllers resolve conflicts in daily operations could help design assistance tools that generate advisories more likely to be accepted and integrated into their workflow. This study introduces a data-driven methodology for identifying and cataloguing air traffic deconfliction instances using historical ADS-B data. By analysing trajectory deviations and their impact on predicted aircraft separations, we extract instances of deconfliction and encode them into a structured dataset. This dataset captures key elements of each event, including sector information, deviated aircraft details, predicted non-deviated trajectories, and surrounding traffic conditions. Our approach facilitates large-scale analysis of air traffic control decision-making, providing a foundation for developing conflict resolution models that better reflect operational practices. This paper details the methodology and process used to construct this dataset.

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