Decision-making tools to enhance the safety of cyclists and inline- skaters at rural midblock crossings
Bibliographic record
Abstract
Transportation agencies have developed various tools to assist planners and engineers in evaluating whether or not a bike path should be grade-separated when crossing a rural highway midblock. In this paper, observed values at a crossing site are compared to theoretical models. The site studied is located on a busy path crossing a 90km/h road, with average daily summer traffic of 11,700 vehicles. Data was obtained by a video camera, traffic counters placed on the cycle path and the road, and by a laser speed detector, aimed at vehicles approaching and once they reach the crossing. A study of gap acceptability was also performed. It revealed gaps too small to allow safe crossing of the road, explaining a very high number of at-risk crossings. The number of bike path users at the crossing was inversely correlated with vehicle speeds, and vehicle drivers were often forced to stop for cyclists and inline-skaters that crossed without priority. Global results applied to theoretical decision models suggested that a grade-separated crossing should be built to increase safety.
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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 | 0.009 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".