Conventional or parking-protected bike lanes? A Full-Bayesian before-and-after assessment
Bibliographic record
Abstract
OBJECTIVE: Biking infrastructure plays a crucial role in ensuring cyclists' safety and encouraging more people to bike. Recently, many North American municipalities started to adopt a new bike lane design, namely the parking-protected bike lane (PPBL), in which the bike lane is placed between the sidewalk and the parking lane. This study aims to assess the safety impacts associated with converting conventional bike lanes (CBLs) to PPBLs. METHODS: To that end, collision and traffic data were collected at 19 street sections from three corridors in Vancouver and Ottawa before and after the conversion. Poisson-Lognormal Linear Intervention model was developed to undertake a Full Bayesian before-and-after analysis to evaluate the change in the frequency of bike-vehicle collisions and other collaterally affected collisions (i.e., total and rear-end collisions) after implementing PPBLs. RESULTS: Reductions of 31.2%, 16.5%, and 4.4% were observed for total, rear-end, and bike collisions, respectively, after implementing the PPBLs, but the results varied significantly depending on the corridor characteristics. CONCLUSION: Overall, PPBLs demonstrated positive impacts on cyclist safety in some corridors, but their performance is highly sensitive to bike path opening density, intersection density, and intersection treatments.
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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 | 0.022 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".