Ensemble-based model to investigate factors influencing road crash fatality for imbalanced data
Bibliographic record
Abstract
The rapid growth of urbanization and motorization has significantly increased traffic crashes, leading to both loss of life and diminished quality of life for crash survivors and their families. Identifying the factors influencing crash fatality is crucial for reducing such incidents. However, traffic crashes are inherently unpredictable, and crash fatality datasets are often imbalanced. This study provides a comprehensive evaluation of various machine learning (ML) techniques to analyze traffic crash fatality using an imbalanced dataset. It is the first to train eight distinct binary classification models: Classification and Regression Trees (CART), Random Forest (RF), Gradient Boosting Machine (GBM), Extreme Gradient Boost (XGBoost), Logistic Regression (LR), Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Naïve Bayes (NB) under three strategies: in isolation, with bagging, and with optimized bagging techniques (Grid Search CV, Random Search CV, and Bayesian Optimization). To handle data imbalance, eight resampling methods were employed, including SMOTE, Random Under-sampling (RUS), Random Over-sampling (ROS), ADASYN, Tomek Links, Near Miss, SMOTETomek, and SMOTEENN. Results show that GBM, combined with Bayesian optimized bagging and RUS, achieved the best performance with a G-mean score of 65.23 and an F1 score of 60.06. This study offers valuable insights into effective ML techniques, data resampling methods, and advanced optimization strategies for imbalanced crash severity datasets.
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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 | 0.003 | 0.005 |
| 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 | 0.001 |
| 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".