IDENTIFYING PASSENGER CORRIDORS ON THE U.S. HIGHWAY SYSTEM USING ATS DATA
Bibliographic record
Abstract
The 1995 American Travel Survey (ATS) represents the most comprehensive survey since 1977 on the long-distance travel of persons living in the U.S. Approximately 80,000 households were surveyed to collect information related to the characteristics of households and trips they made during 1995. The ATS data reveal that U.S. households took nearly 685 million long-distance trips in 1995. Personal-use vehicles (PUV), which include car, van, truck, motorcycle, and recreational vehicles, were the dominant mode of transportation used for long-distance trips. In addition to the comprehensive and accurate coverage of long-distance trip characteristics, the ATS data also include detailed geographic information. The objective of this study is to describe passenger long-distance travel patterns on the U.S. highway system. Based on the geographic travel patterns, major highway transportation corridors can be identified. These corridors are identified in terms of household trips (vehicle trips) and person-trips. The weighted average household trip (vehicle trip) length and the weighted average person-trip length also are provided. Information on three different types of vehicle trips are presented in this paper: (1) domestic PUV trips; (2) domestic bus trips; and (3) highway trips from the U.S. to Canada and Mexico (combined PUV and bus). The resulting vehicle traffic and person-trip flows are presented as flow maps to illustrate the intercity long-distance highway travel patterns within the U.S. For domestic trips by PUV, related information is further categorized by trip purpose and household income levels. Results tabulated for the top 45 intercity (i.e., intermetropolitan) corridors are presented in the appendix.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| 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 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".