Cyclist Perception–Reaction Time and Stopping Sight Distance for Unexpected Hazards
Bibliographic record
Abstract
Cyclist perception–reaction time and deceleration rate have not been previously measured for unexpected hazards, which is a major knowledge gap for the design of safe bicycle facilities. This research aims to determine the distributions of perception–reaction time, deceleration rate, and stopping sight distance for cyclists in response to an unexpected, fixed object in their path that requires an emergency stop. Cyclist braking was measured in response to both unexpected and expected stimuli to balance risk and representation for a total sample of 302 participants. The 85th percentile perception–reaction time for an unexpected hazard was 0.84 s, substantially shorter than in motor vehicle studies and design guidance, possibly due to heightened vigilance when riding a bicycle. In contrast, the 85th percentile deceleration rate was 0.20g (1.96 m/s2), smaller than some design guidance, possibly due to the risk and complexity of hard braking on a bicycle. Lower self-reported confidence when cycling was associated with shorter perception–reaction time and smaller deceleration rate, suggesting increased vigilance and greater caution when braking. Neither age nor gender were significant predictors of perception–reaction time or deceleration rate, although men had higher approach speeds, which increased their total stopping sight distance. These findings provide evidence-based parameter values for design of safe bicycle facilities considering the performance of a diversity of cyclists.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".