Safety evaluation of left-turn priority phasing at Toronto intersections
Bibliographic record
Abstract
Traffic accidents cause a huge loss to the society. According to statistics, 50% of all accidents occur at urban intersections and 47% of these are due to left-turn collisions. Countermeasure Implementation at these locations therefore can play a vital role in the improvement of traffic safety. This study illustrates a methodology for evaluation of urban 4-legged signalized intersections treated with left-turn priority phasing. The methodology is applied to three important collisions types: those due to left-turn collisions; those due to left-turn side impact collisions; and all impact types combined collisions. Data used in this analysis were obtained from the City of Toronto. Safety Performance Functions for left-turn and all impact types combined collisions which were developed by the City of Toronto, were calibrated and used in an empirical Bayesian methodology that was employed to estimate the expected frequency of accidents occurring at each intersection in order to evaluate the effectiveness of left-turn priority phasing in reducing this frequency. The results revealed that left-turn priority phasing can be an effective treatment for addressing and reducing the number of collision at signalized intersections. Flashing advance green phasing is more effective in improving safety for two of three types; all left-turn and all impact types combined collisions. Left-turn green arrow (protected/permissive) phasing is more effective for left-turn side impact collisions. By implementing this type of treatment, the number of crashes and the associated monetary loss to society could be significantly reduced.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".