Evaluating Level of Service at Airport Passenger Terminals: Review of Research Approaches
Bibliographic record
Abstract
Establishing measures to evaluate the level of service (LOS) at airport passenger terminals is of interest to airlines and airport operators. Airport LOS has been evaluated at individual airports, but no standard method or reporting system exists. Airport passenger terminal LOS and capacity have been research topics over the past two decades. More recently, studies have been initiated to identify the passenger terminal problem in general and capacity and service measures in particular. In 1986, FAA responded to concerns about an inadequate understanding of passenger terminal capacity constraints by commissioning TRB to study ways to measure airport capacity. This study recognized that the capacity of any given airport facility cannot be evaluated without defining acceptable LOS values. Currently, however, little agreement exists on analysis methods. No universally accepted way exists to measure LOS for airport terminal buildings. In this regard, various approaches developed by different agencies and researchers are reviewed. This review is useful for professionals interested in applying one of the methods previously developed but not having access to all published information, especially the new approaches. The intent of this review is to motivate new research on the subject, which would facilitate the integration of various existing methods or the development of new approaches.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.014 | 0.019 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".