Spatial modelling of midair collision risk using ADS-B data
Bibliographic record
Abstract
The airspace environment is a system that is expected to continue increasing in complexity with the projected growth of air traffic volumes and the introduction of new types of air vehicles and operations such as uncrewed aircraft. This increase in complexity brings a need for investigating and developing new models of airspace environments as a means of better understanding and managing their constituent parts. This paper presents a methodology for creating a geospatial model of complex airspace environments which can be used to study any geospatially distributed entity that is part of these systems. The methodology leverages Discrete Global Grid Systems (DGGS), a Geographic Information Systems framework often utilized in the fields of geography and urban planning. The usefulness of the model is demonstrated using two case studies investigating the risk factors associated with weather and mid-air collisions in an airspace region of interest. Since such a model needs to be able to work for any type of air vehicle and airspace region in a fully three-dimensional model capable of performing time-varying analysis in a computationally efficient manner, a rudimentary geospatial airspace risk model was also developed which satisfies these requirements. Weather radar data from the National Oceanic and Atmospheric Administration and air traffic data from the OpenSky Network were collected and integrated in the geospatial model and the geospatial airspace risk model was used to calculate the risk of collisions for geospatially distributed points in the airspace for four scenarios of increasing airspace complexity. The results from these four scenarios demonstrate that the proposed methodology can be used to study the risk associated with spatially distributed risk factors for different points in the airspace for any type of air vehicle and airspace region of interest in a fully three-dimensional model that can perform time-varying analysis in a computationally efficient manner.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".