Investigation of the factors affecting the consistency of short-period traffic counts
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
The main intent of this study is to investigate the accuracy of short-duration traffic counts conducted during winter months. The investigation is based on 11-year sample data collected using permanent traffic counters at various locations in Alberta, Canada. Four types of road sites: commuter, regional commuter, rural long-distance, and recreational sites are studied. The sample data constitute six different durations of counts (12-, 24-, 48-, 72-, 96-h, and 1 week) taken during summer and winter months. The coefficient of variation (CV) is used as the relative measure of deviation for counts of different durations to measure the accuracy of short-period traffic counts. The study results indicate that 48-h count seems to be the most cost-effective counting interval during both summer and winter months. It is also found that the lowest values of CV result for counts taken at commuter sites, and the highest values are observed for recreational sites. Frequent changes in temperature and other weather events cause significant variation in traffic volume, which results in an increase in CV values for counts taken during winter months. The application of an adjustment factor to remove the effect of cold and snow from short-period counts is also included in this study. Introduced adjustment factors can reduce the values of CV for all counts taken during winter months. The findings of this study can lead highway agencies to improve the cost-effectiveness of their short-period traffic counting programs.
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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