Estimation of Annual Average Daily Bicycle Traffic with Adjustment Factors
Bibliographic record
Abstract
The purpose of this research was to investigate the estimation accuracy of annual average daily bicycle (AADB) traffic volumes when both daily and monthly adjustment factors (DFs and MFs, respectively) were used. Daily bicycle volume data for a full year were collected at 12 permanent count stations during 2010 in the city of Vancouver, British Columbia, Canada, and used to calculate adjustment factors for bicycle traffic. The factors were applied to estimate the AADB traffic volumes at other count stations where data were available for most of the year. A comparison between the use of MFs and seasonal factors showed that the results supported the superiority of using MFs. Detailed error analyses showed that the lowest errors were attained when the developed factors were applied to the volume data of 2010, which was the same year of the development data. For estimating the AADB with only 1 day of bicycle volume data, daily bicycle volumes were multiplied by both DFs and MFs. A disaggregate error analysis estimated the amount of error attributable to the use of DFs versus MFs. Almost 15% of the estimation error of the AADB could be attributed to the use of DFs, whereas 11% was attributed to the use of MFs. Nevertheless, the overall error of using the two factors together was about 23%. The paper also provides insights on the days and months for collection of data on bicycle volumes. Those insights could improve the design of data collection programs for bicycle traffic.
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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 | 0.003 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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 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".