Taxiing Route Planning for Aircraft on Airport Surface Considering Conflicts With Ground Service Vehicles
Bibliographic record
Abstract
The density and complexity of airport surface traffic increase significantly with the expansion of hub airports. Conflicts may occur due to the concentration of aircraft and ground service vehicle (GSV) movements, leading to safety and congestion issues. Considering the potential conflicts with GSV routes, a strategic aircraft taxiing route planning model is proposed, which integrally optimizes apron area and runway assignments for arriving/departing aircraft, as well as their taxiing routes. Two prioritized objectives are considered for the safety of airport surface traffic and the convenience of passengers. The proposed model is validated first in an illustrative example and then applied in the real‐world case of Shanghai Pudong International Airport, China. The optimal routing plan effectively reduces the conflict points between aircraft and GSVs by 29.2% and 19.0%, respectively, compared with the two typical alternative plans. To assess the impact of different management preferences on the trade‐off among various locations and types of conflicts, a series of experiments are conducted by setting up different conflict weights. The results demonstrate that the optimized plan effectively reduces the number of conflicts with higher weights.
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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 | 0.000 | 0.001 |
| 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 | 0.001 |
| 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".