Exploring Several Accident Risk Factors in Jordan Using Machine Learning
Bibliographic record
Abstract
Traffic accidents cause large human and economic losses yearly, making them a major world challenge. Jordan, one of the developing countries, has immature safety procedures and thereby suffers from the effects of traffic accidents. However, research studies of traffic accidents should be shifted from the traditional toward Artificial Intelligence (AI) and Machine Learning (ML) methodologies that are not able to capture the complex interactions of road crashes. Therefore, in this research, the Random Forest (RF) algorithm has been utilized to predict locally significant risk factors that lead to serious injuries (fatal, serious, moderate, or minor) and then making a better safety decision based on that. The study used 28000 original traffic accident records obtained from the Jordanian Public Security Directorate's traffic accident records. Around 25 characteristics of those records have been analyzed, which include speed, road characteristics, weather, lighting, driver errors, etc. The performance of the used RF model has been validated using 10-fold cross-validation and then compared with other traditional ML algorithms such as logistic regression, Support Vector Machine (SVM), and K-Nearest Neighbors (KNN). The results show the superiority of the RF algorithm with 91% accuracy, compared with 58% for other algorithms. Specifically, an overall F1-score of 0.92, with particularly strong results for fatal and serious cases (F1 = 1.00 and 0.99), has been achieved using RF. The key factors that support the prediction of serious injuries are speed (18%), lighting (12%), and driver age (10%), respectively. These findings not only help the Jordanian Public Security Directorate to release strict regulations that decrease the number of serious traffic accidents, including the adjustment of speed limits on high-risk roads and ensuring adequate lighting in critical zones but also they can be widely considered for helping other developing countries with similar accident records.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".