Pedestrian and Bicyclist Crash Experience at Interchange Ramp Junctions in Ontario
Bibliographic record
Abstract
This paper includes the empirical analysis of crash data involving pedestrian and bicyclists at interchange ramp junctions in Ontario, Canada. While crash rates between vehicles and bicyclists/pedestrians are relatively low, they are difficult to address because of the high speed differential, the complexity of the interchange design, and the high traffic volume environment. This paper first reviews publications about pedestrian and bicyclist crashes at interchanges. Next pedestrian and bicyclist crashes are analyzed by crash location, light condition, severity, and vehicle turning maneuver, off-ramp and on-ramp junction types, and comparisons to vehicle-only crashes. Finally this paper presents its findings of 1) locations with the highest number of pedestrian and bicyclist crashes are also locations with the highest number of vehicle crashes, 2) pedestrian and bicyclist crashes are five times more likely to occur at signalized off-ramps from the freeway than at on-ramps to freeways, 3) direct on-ramps to freeways configurations have a higher frequency of crashes than inner loop on-ramp configurations to freeways. The paper proposes that where capacity allows, restricting right turn on red may have a high cost-benefit ratio for safety at freeway exit ramps improving the safety for pedestrians, bicyclists, and vehicles at exit ramp junctions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 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".