Driver and vehicle type parameters' contribution to traffic safety in UAE
Bibliographic record
Abstract
Former traffic safety studies showed clearly that driver or human factor is a major contributor to road accidents. Hence, to better understand the traffic accident nature it's so vital to analyze all characteristics related to drivers involved in these accidents. This article focuses on this aspect through using a dataset representing UAE traffic accidents in the time interval between 2007 and 2010. A major focus was given in this article to analyzing the relation between traffic accidents and driver citizenship for all types of involved vehicles. This was due to the fact that drivers in UAE came from different backgrounds (over than 100 citizenships) and hence it's so important to identify citizenships with major involvement in road accidents for each vehicle type. This will allow traffic authorities to give special attentions to these citizenships and vehicle types through special traffic awareness programs, fining system or other preventive measures aiming to reduce the accidents frequency and severity levels. Results indicated that for all types of vehicles emirates nationals drivers represent the citizenship with the highest involvement rate in traffic accidents (30.02%) followed by Pakistanis (21.26%) then comes the Indians drivers with a percentage of 11.95%. Light vehicle type traffic accidents statistics shows that a general trend can be seen for all citizenships where there is an increase in the number of traffic accidents over the course of the three years. The main contribution of the paper is its uniqueness in analyzing such accidents database after the implementation of the new unified traffic law in UAE.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".