The Passengers’ Motion Behaviours during the Gate Transfer Process: Models and Analysis
Bibliographic record
Abstract
Once the gate of one departure flight needs to be reassigned, the passengers of this flight should move from the original gate to an alternative gate, which will affect the passengers’ motions near the boarding gates involved in the gate transfer process (e.g., the motion trajectory, the motion time, and so on). Therefore, it is necessary to study the impacts of gate reassignment on passengers’ motions and provide some reasonable suggestions for gate reassignment. However, it is not easy to describe large-scale passengers’ complex dynamic nonlinear interactions, especially involving passengers carrying luggage. Thus, we propose an extended social force (SF) model to describe each passenger’s motion when the flight’s gate is temporarily transferred to one alternative gate, where the proposed model has explicitly considered the interactions among the adjacent passengers and between the passengers and their luggage. The simulation results illustrate that the passengers’ motion directions, the number of passengers with carried luggage, and the passengers’ contact distances will affect the passengers’ motion efficiency during the gate transfer process. In addition, based on the simulation results, we propose some suggestions for gate reassignment from the perspective of the passengers’ motion efficiency, where the suggestions can help administrators better reassign boarding gates.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".