An integrated clustering and Bayesian approach to investigate the severity of pedestrian collisions at highway-railway grade crossings collisions
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This study aims at developing a solid understanding of the contributing factors to pedestrian fatal and injury collisions at highway-railway grade crossings (HRGC), along with the impact of different warning devices that are commonly used at HRGCs. The study utilized integrated Machine Learning and Bayesian models to analyze the United States HRGC collision using the Federal Railroad Administration database between 2009 and 2018. The results demonstrate the association between different factors and the collision severity in each cluster and attempt to explain the inconsistency associated with the impact of some factors, such as weather conditions and pedestrian traits, on collision severity. The results also highlighted the conditions at which the different types of countermeasures and warning devices are most effective and the circumstances that limit their benefits. The results confirmed the benefits of the proposed analysis approach, in which collision data are classified into a group of clusters first before investigating the impact of the different factors on collision severity. The results wills support engineers and planners to develop specific policies and designs that aim at mitigating severe collisions at HRGCs and enhance pedestrian safety.
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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 | 0.001 |
| 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 it