Assessment of Fatal Road Traffic Crashes in Turkey
Bibliographic record
Abstract
In Turkey, thousands of people die in road traffic accidents and hundreds of thousands are injured every year. This study aims to quantify the fatal traffic accident problem in Turkey. Quantifying the scale of the problem will direct the attention of decision-makers and related authorities in Turkey to tackle the traffic accidents’ problem. To accomplish these objectives, accidents’ data from 2009 through 2019 were used. The characteristics of fatal traffic accidents were investigated and models were developed. The accuracy of the model’s performance was evaluated using Root Mean Square Error and the Coefficient of Determination. The results showed that about (9%) of total accidents have resulted in human casualties. The analysis revealed that mistakes of drivers and pedestrians caused 88% and 8% of the total accidents respectively. Drivers' mistakes included violations of the speed limit, right of way and maneuvering rules. The developed models showed a strong correlation between the numbers of injuries and vehicles’ km/year (R2 = 0.84). Finally, the number of casualties in the last two years, showed a decrease as a result of a drop in the mobility rate of heavy vehicles in addition to a slowdown in the growth rate of registered vehicles.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 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 | 0.000 |
| 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".