Air Cargo Transportation Route Choice Analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Using a unique feature of air cargo transshipment data in the Northeast Asian region, this paper identifies the critical factors that determine the transshipment route choice. Taking advantage of the variations in the transport characteristics in each origin-destination airports pair, the paper uses a discrete choice model to describe the transshipping route choice decision made by an agent (i.e., freight forwarder, consolidator, and large shipper). The analysis incorporates two major factors, monetary cost (such as line-haul cost and landing fee) and time cost (i.e., aircraft turnaround time, including loading and unloading time, custom clearance time, and expected scheduled delay), along with other controls. The estimation method considers the presence of unobserved attributes, and corrects for resulting endogeneity by use of appropriate instrumental variables. Estimation results find that transshipment volumes are more sensitive to time cost, and that the reduction in aircraft turnaround time by 1 hour would be worth the increase in airport charges by more than $1000. Simulation exercises measures the impacts of alternative policy scenarios for a Korean airport, which has recently declared their intention to be a future regional hub in the Northeast Asian region. The results suggest that reducing aircraft turnaround time at the airport be an effective strategy, rather than subsidizing to reduce airport charges.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it