Classification of Bicycle Traffic Patterns in Five North American Cities
Bibliographic record
Abstract
This study used a unique database of long-term bicycle counts from 38 locations in five North American cities and along the Route Verte in Quebec, Canada, to analyze bicycle ridership patterns. The cities in the study were Montreal, Quebec; Ottawa, Ontario; and Vancouver, British Columbia, in Canada and Portland, Oregon, and San Francisco, California, in the United States. Count data showed that the bicycle volume patterns at each location could be classified as utilitarian, mixed utilitarian, mixed recreational, and recreational. Study locations classified by these categories were found to have consistent hourly and weekly traffic patterns across cities, despite considerable differences between the cities in their weather, size, and urban form. Seasonal patterns across the four categories and in the cities also were identified. Expansion factors for each classification are presented by hour and day of the week. Monthly expansion factors are presented for each city. Finally, traffic volume characteristics are presented for comparison purposes.
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How this classification was reachedexpand
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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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 teacher head, 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".