Safety of Channelized Right-Turn Lanes for Motor Vehicles and Pedestrians
Bibliographic record
Abstract
The paper presents the results of research undertaken to evaluate how the safety performance of intersection approaches with channelized right-turn lanes compares with that of intersection approaches with conventional right-turn lanes or shared through and right-turn lanes. Crash data for nearly 400 intersection approaches in Toronto, Ontario, Canada, including intersection approaches with channelized right-turn lanes, conventional right-turn lanes, and shared through and right-turn lanes, were analyzed to compare the safety performance of the three right-turn treatment types. The research results indicate that intersection approaches with channelized right-turn lanes appear to have similar motor vehicle safety performance as approaches with conventional right-turn lanes or shared through and right-turn lanes. This result was found both at the downstream end of the channelized right-turn lane (where the right-turning vehicle merges with the cross street traffic) and at the upstream end of the channelized right-turn lane (where the right-turning vehicle begins the right-turn maneuver). Intersection approaches with channelized right-turn lanes also appear to have similar pedestrian safety performance as approaches with shared through and right-turn lanes. Intersection approaches with conventional right-turn lanes have substantially more pedestrian crashes (approximately 70% to 80% more) than approaches with channelized right-turn lanes or shared through and right-turn lanes.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".