U.S. International Travel and Transportation Trends: 2006 Update
Bibliographic record
Abstract
This report presents data on international travel to and from the United States. It combines data from a variety of sources (see box) to provide a more complete picture of U.S.-international travel than is available from individual sources. This report breaks out travel trends (inbound and outbound) with overseas (noncontiguous) countries and our North American neighbors, Canada and Mexico, which account for the greatest number of foreign travelers to the United States. A large number of people travel to and from the United States each year, making use of U.S. and foreign transportation carriers and infrastructure and generating a large amount of economic activity. The magnitude of this travel, involving nearly 340 million visits into and out of the United States in 2004, has far-reaching implications for planning transportation infrastructure, for tourism-related economic development, and for security, both in terms of terrorism concerns and planning for a possible global pandemic. The majority of the travel data in the report covers the period 2000 through 2004, allowing for comparison of international travel in the year immediately preceding and three years following the September 11, 2001 terrorist attacks. Compared to 2000, there were notably fewer U.S.-international trips taken from 2001 to 2004. The lowest period of international travel was in 2003. A prior RITA/BTS report in this series focuses on the travel trends between 1990 and 2000; however, for the convenience of the reader an appendix has been included to show trends from 1990 through 2004.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.019 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.012 |
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".