Accounting for Weather Conditions When Comparing Multiple Years of Bicycle Demand Data
Bibliographic record
Abstract
In order to evaluate the impacts of bicycle infrastructure and policy, municipalities often seek to determine whether cycling demand has increased or decreased from one year to the next. They do so by analyzing bicycle demand data, such as bicycle counts from a permanent counter, or trip count estimates from an origin-destination travel diary survey. The problem with this approach is that bicycle demand is dependent on weather conditions. A particularly warm or cold season can mask or inflate inherent changes in cycling demand, and direct comparison of raw bicycle count data can produce misleading results. This paper seeks to address this challenge by proposing a framework through which bicycle demand data can be adjusted to account for weather conditions. A regression model is calibrated and used to estimate counts based on observed and average weather conditions. These estimated counts are used to calculate adjustment factors which reflect the extent to which weather increased or decreased cycling demand. In two case study applications, this framework is applied to data from permanent counters and from an origin-destination survey in the City of Ottawa.
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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 | 0.014 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".