International travel patterns: exploring destination preferences and airfare trends to and from the USA
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
Approximately one quarter of all U.S. air-passenger trips (involving US airlines only) are to and from foreign destinations, which accounted for around 4.5% of total US person-miles in 2019. Travel demand modeling and US travel surveys often overlook this overseas travel. Therefore, this study assesses travel demand, patterns, and costs (in time and money) between major US and foreign airports worldwide, as well as ground trips to Mexico and Canada, using 2019 DB1B flight ticket data, the 2016–2017 National Household Travel Survey (NHTS), and border crossing data. A model of trip distribution, from 334 US airports to 1,028 foreign airports, shows how trip flows fall by about 41% with every 7-hour increase in flight start-to-end time. Destinations hosting tourist attractions (e.g, London, Barcelona, Milan, Paris, Dubai) are also a practically significant variable in the model, increasing flows by 48%. Flight fares (for one-way itineraries) increase by $0.078 per mile for coach class and $0.163 per mile for business class and higher, according to feasible generalized least-squares models. These fares are higher for English-speaking destinations than non-English-speaking destinations, as well as for trips from April to June (as compared to January to March with similar distances and seating types).
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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