A driving risk prediction method for elderly drivers considering data imbalance and feature extraction
Bibliographic record
Abstract
Abstract With the aging of society, the increase in the number of elderly drivers poses a potential hazard to road traffic safety. Therefore, accurately predicting the severity of possible traffic accidents of elderly drivers is crucial to ensure the safety of drivers and passengers. In this paper, a hybrid model based on the CTGAN-ResNet-XGBoost network is proposed for classifying the severity of the accidents of elderly drivers. The model was trained and tested using traffic accident data of the United States from 2018–2022. The hybrid model first generates a small amount of categorical data via the Conditional Tabular Generative Adversarial Network to address the dataset's category imbalance. Then, the balanced dataset is transformed into feature images using the DeepInsight method and feature extraction is performed using the residual neural network to improve the feature recognition ability of the classification model. Finally, the XGBoost model is used to classify the severity of the accident and the SHAP method is used to analyse the main features affecting the accident. The superior performance of the hybrid model is verified through experimental comparative analysis. The experimental results show that the hybrid model has a significant advantage in the prediction of driving risk for elderly drivers, that the causes of accidents for elderly drivers are different from those for younger drivers and that the characteristics of speed, seat belt use and driver's age are the main factors affecting the severity of accidents. The results of this study improve the accuracy and reliability of traffic accident severity prediction and provide more scientific support for traffic safety management.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".