Traffic Use of Rest Areas on Rural Highways
Bibliographic record
Abstract
Rest areas provide the occupants of passenger vehicles and the operators of heavy vehicles an opportunity to use a restroom, walk around, stop for a meal, sleep for a while, or pause to use a cellular phone. These activities have a direct impact on several aspects of the design of rest areas, from parking to facility sizing, water needs, and wastewater generation and handling. All these components are directly influenced by one critical factor: entering traffic volumes. The study presented here used data from 44 study sites to examine the amount of traffic that used rest areas, expressed as the percentage of the main-line hourly volume that entered the rest area, as well as the effect of many underlying variables that were believed to affect the use of rest areas. The study found that the average rate of rest area use by main-line traffic for different highway categories (high- and low-volume Interstates and arterials) varied from 8.4% to 12.3%. For main-line traffic entering rest areas, the overall average rate of use was approximately 10% and the overall 85th percentile was about 15%. The study identified two peaks during the day for the percentage of main-line traffic using the rest area, but vehicular counts at rest areas showed only one peak at about midday. Given this peak demand, the midday period should be considered in the planning and design of rest area facilities. For the majority of rest areas examined, the average rate of use during the midday period varied roughly from 13% to 17% of main-line traffic.
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.
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 | 0.001 | 0.001 |
| 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) | 0.001 | 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".