Temporal analysis of factors affecting injury severities of expressway rear-end crashes during weekdays and weekends
Why this work is in the frame
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Bibliographic record
Abstract
High fatality rates in frequent rear-end crashes have underscored significant safety concerns in China. This study aims to explore the mechanisms and determinants of rear-end crashes, with a particular focus on the factors influencing crash severity during weekdays and weekends (W-W). Employing the Random Parameter Logit Model (RPLM) to account for variability in data, we analyzed W-W rear-end crashes on the Beijing-Shanghai Expressway in Jiangsu province from 2017 to 2019, considering three severity levels: no injury, minor injury, and severe injury. Our comprehensive analysis covered variables from temporal, roadway, vehicle, crash, and environmental categories, alongside calculating the marginal effects of each significant variable on crash severity. Findings reveal temporal instability over the three-year period and notable differences in W-W crash severity. Out of all variables, four displayed random parameter characteristics, indicating potential interactions that influence crash outcomes. Specifically, our results indicate that rear-end crashes involving three or more vehicles on bridges are more likely to result in casualties. Interchange segments typically saw no injuries in two-vehicle crashes. Speeding during winter or on sunny days significantly increases the risk of injuries and fatalities. Furthermore, rear-end crashes in interchange areas during winter are particularly prone to causing injuries. These findings offer guidance for the development of effective safety countermeasures targed at different pediods.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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