SAFETY ASSESSMENT OF DIFFERENT BIKE INFRASTRUCTURE TYPES
Bibliographic record
Abstract
This thesis comprises two studies that investigated bike infrastructure safety in North America. The first study conducted a corridor-level analysis to quantify the safety of different bike infrastructure types and assess the influence of corridor-specific characteristics on their performance. Using a Poisson-lognormal Full Bayesian model, the study analyzed fatal and injury bike-vehicle collisions on over 7800 corridors in Toronto, Canada. The findings revealed that bike infrastructure effectively reduces bike collisions, with cycle tracks demonstrating superior safety benefits due to the physical separation of cyclists from vehicular traffic. Cycle tracks were found to be particularly effective on long corridors with fewer intersections as bike-vehicle interactions along the corridor are minimized. Signed routes were safe on low-volume and low-speed roads, while bike lanes are more suited for a short section of high-volume corridors with a high intersection density. The second study assessed the safety of parking-protected bike lanes (PPBL), a new concept that is rapidly growing in North America. Utilizing data from nineteen street sections in Vancouver and Ottawa, the study developed a Full Bayesian Before-and-after model to evaluate the safety impacts of converting traditional painted bike lanes to PPBLs. The results indicated a significant reduction of 31.2% in total collisions after PPBL implementation. However, the effects of PPBLs on cyclist safety were found to be sensitive to factors such as bike path opening density, intersection density, and intersection treatments. In roads where proper intersection treatments and minimal protection of PPBL openings can be provided, painted bike lanes can be converted into PPBLs, and significant safety benefits can be expected. The findings of this thesis offer practical guidance for city planners and policymakers regarding the safety implications of different bike infrastructure types and the most appropriate conditions to implement them, which supports bike safety enhancement initiatives and attracts more people to bike.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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 | 0.000 |
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".