Robust Trajectory Prediction Using Random Forest Methodology Application to UAS-S4 Ehécatl
Why this work is in the frame
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Bibliographic record
Abstract
Accurate aircraft trajectory prediction is fundamental for enhancing air traffic control systems, ensuring a safe and efficient aviation transportation environment. This research presents a detailed study on the efficacy of the Random Forest (RF) methodology for predicting aircraft trajectories. The study compares the RF approach with two established data-driven models, specifically Long Short-Term Memory (LSTM) and Logistic Regression (LR). The investigation utilizes a significant dataset comprising aircraft trajectory time history data, obtained from a UAS-S4 simulator. Experimental results indicate that within a short-term prediction horizon, the RF methodology surpasses both LSTM and LR in trajectory prediction accuracy and also its robustness to overfitting. The research further fine-tunes the performance of the RF methodology by optimizing various hyperparameters, including the number of estimators, features, depth, split, and leaf. Consequently, these results underscore the viability of the RF methodology as a proven alternative to LSTM and LR models for short-term aircraft trajectory prediction.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it