Safety Evaluation of Freeway Speed-Change Lanes Based on Crashes and Simulated Conflicts
Bibliographic record
Abstract
To facilitate the evaluation of the safety performance of freeway merge, diverge, and weave areas, conventional crash-based Safety Performance Functions (SPFs) were developed using generalized linear models (GLM) with a negative binomial (NB) error structure. However, crash-based SPFs may not take into account all factors that contribute to the crashes. The use of simulated conflicts as a surrogate safety measure to predict crashes can address this issue and provide recommendations for the designs and traffic control strategies. This approach was explored by using Surrogate Safety Assessment Model (SSAM) and VISSIM software to generate and analyze conflicts for merge areas on Ontario freeways. Crash-conflict integrated SPFs with different Time to Collision (TTC) thresholds were then developed and compared. Their predictive capabilities were also evaluated. To complement this analysis, the transferability of US crash prediction models to Ontario data was evaluated and the goodness-of-fit of these models was explored.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".