Effects of emergency escape ramps on crash injury severity reduction on mountain freeways: A case study in China
Bibliographic record
Abstract
Emergency escape ramps (EERs) is an infrastructure of mountain freeways to stop runaway vehicles. As the last defence of vehicle occupants’ safety, the performance of EERs in reducing crash injury severity is a concern for stakeholders of road safety. Based on crash records collected on a mountain freeway equipped with five EERs, this study compared the injury severity of crashes on EERs and other road sections, and identified the factors that significantly influence injury severity in the two conditions. Estimations of the parameter coefficients and marginal effects of a random parameters ordered probit model were used to infer EER performance under the impacts of various factors. The results confirm the effectiveness of EERs on the reduction of crash injury severity. The protection function of EERs is weakened by nighttime, the rollover status of crashed vehicle, multi-vehicle collisions, improper design or installation of the roadside infrastructure, drivers’ unfamiliarity with local driving conditions, and crashed vehicle weight. The paper compares the findings with those of previous studies and proposes some recommendations to improve EER performance for occupant and property protection on mountain freeways.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".