FedEx and UPS Network Structure and Accessibility Analysis Based on Complex Network Theory
Bibliographic record
Abstract
With the expansion of the global air cargo transport system, the operating structure of air cargo has become increasingly separate from passenger counterpart, forming an independent organization model. Despite the Chinese air cargo capacity has grown exponentially in the past, its network is still in its infancy. FedEx and UPS have well‐established air cargo networks and have operated effectively on both international and domestic scale; thus, understanding the structure and evolution of their air cargo networks is of a high reference value. In conjunction with the division of US regions from the United States Geological Survey (USGS), this paper refers to FedEx and UPS as FEPS and analyzes its topological structure, complexity properties, and air cargo accessibility by using social network analysis (SNA) and accessibility evaluation methods. The results suggest that (1) the structure of the FEPS air cargo network is in the highly developed states and has the typical “small‐world” and “scale‐free” network characteristics; (2) the degree centrality values for the nodes in the FEPS network suggest that the network complexity has increased; (3) airports in Memphis (MEM), Louisville (SDF), Indianapolis (IDN), and Ontario (ONT) are the major hubs with both high centrality values and air cargo accessibility; and (4) the FEPS network presents a unique hub‐and‐spoke structure compared with the passenger counterpart.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".