Quality Measure of Short-Duration Bicycle Counts
Bibliographic record
Abstract
The average annual daily bicyclists (AADB) measure is commonly used in research and practice as a metric for cycling studies, such as bike ridership analysis, infrastructure planning, and injury risk. It is estimated in one of two ways: by averaging the daily cyclist totals measured throughout the year with a long-term automated bicycle counter, or by using a long-term bicycle counter to extrapolate data from a short-term counting site. Unfortunately, extrapolation of a short-term bicycle counting site can produce inaccurate AADB estimates as a result of different error sources; the range of possible error is highly correlated to several characteristics of the short-term count, such as the counting period, flow intensity, and time of year. This paper proposes a simple method to estimate the quality of a short-term count through a single metric combining five factors associated with the count variation: duration, average demand, time of year, stability, and correlation with the reference count. The method is validated with the use of a relatively large data set of automated bicycle counts. The quality measure, with a range from 0 to 10, is negatively correlated with the absolute relative error (ARE) of the AADB estimation. Results show distinct ARE distributions for different quality measure classes. The average ARE for the lowest quality class is 13.5% compared with an average ARE of 3.0% for the highest quality class. The maximum ARE (95% confidence) is 35% for the lowest quality class compared with 7.5% for the highest quality class.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".