Development of a Map-Matching Algorithm for the Analysis of Aircraft Ground Trajectories using ADS-B Data
Bibliographic record
Abstract
View Video Presentation: https://doi.org/10.2514/6.2023-3758.vid This paper presents the results of the development and validation of a tool created at the LARCASE laboratory for the analysis of aircraft ground trajectories at a given airport using ADS-B data. This tool can automatically generate a graph structure of any airport using public data available on OpenStreetMap. In addition, attributes are also defined to provide specifications for each segment defining the graph, such as segment type, segment name, bearing, distance, and speed limit. The tool also includes a map-matching algorithm that allows users to determine aircraft positions at an airport or reconstruct trajectories for statistical analysis. The map matching algorithm is based on a Hidden Markov Model process and can be used “on-line” or “off-line”. An analysis of over 70 simulated trajectories showed that the algorithm was accurate to within 97-99%. The results also showed that the algorithm was able to process over 100 trajectory data points in less than 2 seconds, which is very fast. Finally, the algorithm was also tested using ADS-B data collected from FlightRadar24.com, and the results obtained were very good.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.006 |
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".