Data-Driven Modeling of Systemic Air Traffic Delay Propagation: An Epidemic Model Approach
Bibliographic record
Abstract
To better understand the mechanism of air traffic delay propagation at the system level, an efficient modeling approach based on the epidemic model for delay propagation in airport networks is developed. The normal release rate (NRR) and average flight delay (AFD) are considered to measure airport delay. Through fluctuation analysis of the average flight delay based on complex network theory, we find that the long-term dynamic of airport delay is dominated by the propagation factor (PF), which reveals that the long-term dynamic of airport delay should be studied from the perspective of propagation. An integrated airport-based Susceptible-Infected-Recovered-Susceptible (ASIRS) epidemic model for air traffic delay propagation is developed from the network-level perspective, to create a simulator for reproducing the delay propagation in airport networks. The evolution of airport delay propagation is obtained by analyzing the phase trajectory of the model. The simulator is run using the empirical data of China. The simulation results show that the model can reproduce the evolution of the delay propagation in the long term and its accuracy for predicting the number of delayed airports in the short term is much higher than the probabilistic prediction method. The model can thus help managers as a tool to effectively predict the temporal and spatial evolution of air traffic delay.
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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 | 0.001 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".