An exploration of drivers’ lane position after adding buffered cycling lanes in Guelph, Ontario
Bibliographic record
Abstract
Dedicated cycling infrastructure, such as a buffered cycling lane, is implemented more frequently with the goal of improving cyclist safety by decreasing cyclist-vehicle interactions. While previous research has focused on evaluating driver lane position through passing events (when drivers overtake slower cyclists), little research has evaluated how drivers interact with novel cycling infrastructure in the absence of cyclists. Through an analysis of instrumented vehicle data from an on-road study in Guelph, Ontario, this study compares driver behaviors before-and-after modifying an existing cycling lane into a cycling lane with a painted buffer. It was found that drivers were significantly further from the marking of the cycling lane by an average of 31.6 cm when there was a traditional painted cycling lane, as opposed to a buffered cycling lane. This difference was greater than the change in vehicle lane width (narrowed on average by 22.7 cm). However, this may not change overall distance from cyclists when accounting for additional space from the buffer. Drivers did not differ in the standard deviation of their lane position, or in their speeds, between the two types of cycling lane. Findings from this research have implications for decisions regarding infrastructure and the development of automated driving systems.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".