Crash Modification Factors for Changes to Left-Turn Phasing
Bibliographic record
Abstract
This study estimated crash modification factors (CMFs) from before–after evaluations of two treatments targeted at reducing left-turn crashes at signalized intersections: (a) changes from permissive to protected–permissive phasing and (b) the implementation of a flashing yellow arrow for permissive left turns. Results of the first evaluation—based on 59 intersections in Toronto, Ontario, Canada, and 12 in North Carolina—indicated a substantial reduction in left-turn opposing through crashes, especially at intersections at which more than one leg was treated, and a small percentage increase in rear-end crashes. For the second evaluation (the implementation of the flashing yellow arrow)—based on data from 51 signalized intersections in Oregon, Washington State, and North Carolina—the results indicated a safety benefit at locations with some kind of permissive left-turn operation before and a disbenefit at locations that had a protected-only operation before. The study estimated the standard deviation of the distribution of the CMF in addition to the conventionally estimated standard error of the mean CMF value. For several CMFs, the standard deviation of the distribution was larger than the standard error of the mean value of the CMF and indicated a substantial variation in the effect of the treatment across different sites. This finding indicates the need for further research into the development of crash modification functions instead of CMFs and for the use of large treatment databases to undertake a more extensive disaggregate analysis of the safety effects. The finding also emphasizes the importance of providing a more explicit consideration of CMF variability in future editions of the Highway Safety Manual.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".