Optimum Design and Operation of Airport Passenger Terminal Buildings
Bibliographic record
Abstract
The standard procedures for the design and operation of airport passenger terminal buildings (PTBs) often lead to either high operating and maintenance costs or passenger dissatisfaction. A new philosophy of planning, design, and operation that is based on optimum resource utilization and passenger flow management and control was initiated. An optimum resource utilization model was developed on the basis of important issues such as an early association of physical and operational plans, the stochastic nature of airport demand, the long-term costs of over- and undersupply of PTB facilities, performance measures, and utilization of scarce resources. Three submodels were developed as part of the optimum resource utilization model, that is, the simulation, optimization, and flow management and control models. An object-oriented simulation model, which consists of a set of simple submodels and nodes, was developed to perform as a real-world airport terminal. The optimization model will provide a list of optimum required resources for all predefined segments of the PTB. A real-time flow management and control model was developed, in which the PTB operator would be able to respond to preplanned or spontaneous events.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".