Design Guidance for Channelized Right-Turn Lanes
Bibliographic record
Abstract
This report documents and presents the results of research to develop design guidance for channelized right-turn lanes. Observational field studies were conducted at 35 intersection approaches in cities to assess pedestrian crossing behavior, motorist yield behavior, and the interaction between pedestrians and motor vehicles at channelized right-turn lanes. Simulation modeling was performed to quantify the traffic operational benefits of channelized right-turn lanes with various types of traffic control and to compare the delay reduction of channelized right-turn lanes and conventional 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/right-turn lanes, were analyzed to compare the safety performance of the three right-turn treatment types. The research results indicate that channelized right-turn lanes have a definite role in improving operations and safety at intersections. However, to achieve these benefits they should have consistent design and traffic control and should be used at appropriate locations. The research provides design guidance for channelized right-turn lanes that addresses geometric elements such as crosswalk location, special crosswalk signing and marking, island type, radius of turning roadway, angle of intersection with cross street, acceleration and deceleration lanes, and traffic control.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".