Air Transportation Infrastructure Robustness Assessment for Proactive Systemic Risk Management
Bibliographic record
Abstract
A key attribute of resilience, robustness serves as a predictor of infrastructure system performance under disruptions, thus informing proactive infrastructure risk management. A literature review indicated that previous studies did not consider some key factors that can influence the robustness of air transportation infrastructure networks (ATIN) and thus their (system-level cascade) systemic risk management processes. In this respect, the current study first assesses existing methodologies and then develops a new methodology to quantify the robustness of ATIN. Specifically, based on integrating travel time and flight frequency, the study develops alternative best-route and link-weight approaches to assess key ATIN robustness measures and relevant operating cost losses (OCL). In order to demonstrate the practical use of the developed methodology, the robustness and the associated OCL of the Canadian domestic air traffic network are evaluated under random failures (i.e., disruptive events that occur randomly) and targeted threats (i.e., disruptive events that occur deliberately). The analysis results show that the network robustness is influenced by the utilized evaluation approach, especially after 20% of the network components become nonoperational. Overall, the methodology developed within this study is expected to provide ATIN policymakers with the means to quantify the network robustness and OCL, and thus enable ATIN resilience-guided proactive risk management in the face of natural or anthropogenic hazard realizations.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".