Examination of the Temporal Stability of Daily and Monthly Adjustment Factors of Bicycle Traffic
Bibliographic record
Abstract
Monthly adjustment factors (MFs) and daily adjustment factors (DFs) are typically calculated using complete annual data from permanent count stations. These factors are applied at other locations where short-term counts are collected using temporary counters. In this research, the long-term temporal stability of MFs and DFs for bicycle traffic was investigated. Annual daily count data collected from permanent count stations in Vancouver, Canada between 2010 and 2019 were used in the study. A longitudinal analysis of MFs and DFs in the 10 years was undertaken. The coefficient of variation (CoV) of the MFs of the 10 years was found to be less than 12% for the months between April and September, indicating the potential temporal transferability of these factors from one year to a future year. The other 4 months of the year showed a higher CoV with December being the highest (i.e., 25%). As for the DFs, it was found that, depending on the day of week, weather, and month, substantial variation could exist in the calculated factors. Nevertheless, factors developed for dry weekdays in the summer months were shown to exhibit the lowest variability over the 10 years and as such these factors were deemed transferable from one year to another. The results presented in this paper could be beneficial for transportation planners and traffic engineers managing traffic monitoring programs of cycling traffic for which the potential for transferring historical adjustment factors to future years was demonstrated.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".