Effects of emergency escape ramps on crash injury severity reduction on mountain freeways: A case study in China
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
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.
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.
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 | 0.001 |
| 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 it