Probability-Based Method to Support Airport Curbside Level of Service Studies
Bibliographic record
Abstract
: Air transportation industry is challenged to address airport curbside delay problem that affects landside service quality and can potentially impact check-in operation. Methodological advances guided by industry requirements are needed to support curbside improvement studies. Existing methods require verification of assumptions prior to application or need expensive surveys to acquire data for use in microsimulations. A probability-based macrosimulation method is advanced for the evaluation of level of service and capacity of the curbside processor. Following problem definition and literature review, the theoretical foundation of the method is described as the simulation of the stochastic balance of demand and available curb space for unloading/loading task. The method meets the planning and operation requirements with ability to analyse conditions commonly experienced at the curb area. Example applications illustrate the flexibility of the method in evaluating existing as well as planned facilities of diverse designs and sizes. The developed method can contribute to curbside processor delay reduction and due to the macroscopic nature of the method, the data requirements can be met by an airport authority without costly surveys.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".