Before–After Evaluation of Left-Turn Lane Extension Considering Injury Severity and Collision Type
Bibliographic record
Abstract
Left-turn lanes are commonly used to provide space to accommodate vehicle deceleration and provide adequate storage of turning vehicles. The objective of this study is to evaluate the safety effectiveness of extending the length of left-turn lanes at signalized intersection approaches. Five years of collision data including injury severity and collision type from three treatment sites and 31 comparison sites in the City of Surrey, Canada were used in the study. The analysis focused on target crashes including left-turn-related rear-end and left-turn-related sideswipe collisions. A full Bayesian (FB) before–after analysis was conducted for all collisions, severity levels, and collision types. Multivariate Poisson–lognormal linear intervention models were used for the analysis. The treatment effectiveness index was calculated to quantitatively measure the effectiveness of the safety treatment. The FB before–after results showed that the treatment-related collisions were reduced by 57.4% following the implementation of extended left-turn lane. The reduction in injuries and fatalities collisions (63.8%) was greater than that in property damage only collisions (55.7%). The decrease in rear-end collisions (62.8%) was greater than that in sideswipe collisions (58.1%). The findings indicate a remarkable improvement in safety after the length extension of the left-turn lane.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".