Crash Modification Functions for Installation of Left-Turn Lanes at Signalized Intersection Approaches
Bibliographic record
Abstract
This paper presents the results of a study that developed crash modification (CM) functions for installing left-turn lanes at signalized intersection approaches. CM functions were obtained from a longitudinal before–after safety study that accounted for treatment location characteristics (heterogeneity). This approach for developing CM functions has several advantages over the commonly used cross-sectional evaluations, which have several statistical shortcomings. The developed CM functions incorporate a time variable to acknowledge that the safety treatment effects do not occur instantaneously but are spread over future time; this result was achieved with a nonlinear intervention model with the full Bayes method. Twelve treatment sites were selected for the evaluation, along with 67 comparison sites. The treatment included the addition of one or more LT lanes for each intersection. The analysis showed significant safety improvements for fatal-plus-injury and total collisions but statistically nonsignifica...
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".