Research on the Robustness of Air Transportation System Based on Complex Network
Bibliographic record
Abstract
Air transportation has the characteristics of fast transportation, high transportation efficiency and independent of ground conditions, so the economic benefits brought by air transportation greatly exceed those of other transportation modes. However, the safety of the air transportation system is constantly challenged. In recent years, the natural environment has changed due to rapid economic development, and meteorological disasters have occurred from time to time. In addition, flight cancellations due to airline aircraft turnarounds, technical aircraft failures, and traffic jams at airport terminals have become commonplace. To address the problem of insufficient stability of China's air transportation system, cities and flights with civil airports existing by the third quarter of 2022 are taken as the research objects, and the network model of China's air transportation system is constructed by using Ucinet software with the cities where airports are located as nodes and regular routes between airports as connecting edges. Then, robustness simulation experiments are conducted by random and intentional disruptions to analyze the extent to which the network can maintain its normal operation after the nodes in the network are down. The conclusions show that the robustness of the Chinese air transport network is better than that of the intentional disturbance in the case of random disturbance. Finally, suggestions for countermeasures that can improve the stability of the air transportation system are given.
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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 | 0.006 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".