Estimating Intersection Annual Average Daily Bicycle Traffic from 8-hour Turning Movement Counts
Bibliographic record
Abstract
This thesis outlines a set of procedures for estimating annual average daily bicycle traffic (AADB) from one day, 8-hour turning movement counts (TMCs). Factoring methods for annualizing short duration counts have been long established for motor vehicle traffic, and more recent research has adapted many of these methods to pedestrians and bicyclists. However, much of this research has been concerned with estimating AADB on dedicated cycling facilities. Less has been done to transfer these methods to estimating cyclist activity at intersections, even though it would be valuable to measure cyclist exposure for network safety analysis and for broader planning purposes. TMCs represent a valuable potential source of data for this purpose, as it is common for North American jurisdictions to regularly collect them as part of ongoing traffic monitoring programs. Sets of video monitoring unit (VMU) data from Milton, Ontario, and Pima County, Arizona, were used to evaluate whether existing methods could be appropriately applied to 8-hour TMCs. Several updates to conventional estimation methods were proposed to account for the differences between TMCs and “conventional” cyclist counts. Additionally, methods are proposed for filtering VMU data; and for matching short-duration count locations to empirical factor groups using their land-use and physical characteristics. The resulting set of procedures could be implemented by transportation agencies using data which they may already be collecting to generate estimates of cyclist activity at any intersection in their jurisdiction, although further work is likely needed to improve estimation accuracy, especially at low-volume locations.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".