System Dynamic Model for Simulating Aviation Demand: Baghdad International Airport as a Case Study
Bibliographic record
Abstract
The aviation authorities have long been impacted by fluctuations in demand, which are often caused by the aviation industry's cyclical nature. It is affected mainly by many endogenous or exogenous variables. Despite that, the airport authorities and air carrier management make significant efforts to deal with fluctuating demand. This paper analyzed the factors influencing demand at Baghdad International Airport, based on the pertinent local socio-economic data such as "population size," "GDP," and "terrorism effect," as well as system-based factors related to aviation activities and airport characteristics for the past ten years, to develop a system dynamics simulation model by which the causes of fluctuation are highlighted in order to predict the magnitude and timing of the increment or decline in an offer to minimize losses to all parties in the airport system. The simulation results demonstrated very high goodness of fit with the actual data, producing R2 values of 0.865 and 0.86 for the departing and arriving passengers, respectively. Even though Iraq's unstable political and economic situation led to the interaction between the different demand drivers, external factors have a bigger effect on the country's need for air travel, causing demand shocks that take a long time to recover from.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".