Modeling driver injury severity using machine learning algorithms
Bibliographic record
Abstract
This study planned to predict and analyze the driver injury severity (DIS) using 12 machine learning (ML) algorithms. Police reports of single- and two-vehicle accidents that occurred during 2011–2020 in the two cities of India (Itanagar and Imphal) were used in this study. The best-performing model to predict the DIS for Itanagar was Gradient Boosting Trees (GBT). “Causes of Accident” variable had shown maximum impact on the DIS. In the case of Imphal, it was the GBT, Extra Trees, and Random Forest models across all k-fold cross-validation for train ratios 0.70, 0.80, and 0.90, respectively. “Causes of Accident” and “Vehicle Type” had shown maximum impact on the DIS. These results reveal that the ML models can be applied in hilly areas to predict and identify the important factors that affect DIS. Transportation authorities can analyze road accident data using these models while implementing various road safety measures.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".