A Tactical Conflict Detection and Resolution Method for En Route Conflicts in Trajectory-Based Operations
Bibliographic record
Abstract
In trajectory-based operation (TBO), the four-dimensional trajectories (4DTs) of aircraft are shared with all flight-related stakeholders, which makes flights visible and controllable and helps flights arrive at a location within a fixed time range. With the technical support from TBO, the operation of flights in a route sector will be more efficient and the air traffic volume will increase. However, more flights will also result in more flight conflicts. In this study, a deterministic conflict detection and resolution (CDR) module is established to assist air traffic controllers in detecting and resolving high-density conflicts rapidly and in advance. In the conflict detection (CD) submodule, a spatial data structure with low time complexity, the R tree algorithm, is used. R tree can effectively reduce the comparison number between the 4DTs of all aircraft. The experiment results show that the computing time of the R tree presents a logarithmic curve with the increase in the number of aircraft and the efficiency of the CD is more significantly improved. In the conflict resolution (CR) submodule, considering the aircraft performance and terrain constraints, the Monte Carlo tree search (MCTS) algorithm is proposed to solve the problem of huge search space and to quickly provide an effective resolution policy for pairwise conflict. The simulation results in dense airspace indicate that the MCTS-based CR algorithm has good performance in terms of safety and efficiency.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".