Analysis of crash severity at intersections and roundabouts using ordered and generalized ordered probit models
Bibliographic record
Abstract
Accidents are considered as one of the leading causes of death around the world. Investigating the causes of various accidents, identifying the primary factors and accident blackspots can be used to prevent severe injuries and the associated costs and casualties. Many studies are conducted around the world to investigate the impacts of accidents and to identify the main factors. This study investigates the accidents and the severity of their injuries at intersections and squares. Accident data related to Zanjan is used to assess the severity of accidents at urban squares and intersections, which are modeled by SAS software. For this purpose, two models of OP and cumulative Probit are used. The results show that the cumulative probit model is better able to interpret the dependent variable based on the independent variables than the OP model. Based on the overall results of the models, it can be seen that the occurrence of an accident in daytime compared to nighttime, on holidays compared to non-holidays and in winter compared to other seasons, reduces the injury severity. Heavy vehicles reduce the injury severity compared to cars, while two-wheeled vehicles, including motorcycles and bicycles, increase the injury severity. Accidents at intersections also increase injury severity.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".