A Rural Road Accident Probability Model Based on Single-Vehicle Hazard Properties including Hazard Color and Mobility: A Driving Simulator Study
Bibliographic record
Abstract
Road safety has recently been considered an important issue in the country. Single-vehicle accident statistics show the importance of this issue. From a safety viewpoint, drivers need to have a reasonable time window for hazard recognition and reaction; therefore, the hazard has to be in sight from a distance preferably longer than the standard minimum stopping sight distance. Nevertheless, if the roadside configuration makes the sight available for a very long distance, the hazard properties are the ones defining the visibility. The hazard size, color, and mobility are some of the most important hazard properties, which may mainly interact with ambient light (like being day or night) and driving speed. In this research, effect of hazard properties on driving accident likelihood was investigated in a condition that enough recognition and reaction time window was available for the driver to provide a ceteris paribus experiment. To fulfil that in a safe experiment condition, a driving simulator was used to test the behavior of 90 licensed drivers encountering an average of 14 hazards with various sets of properties. Based on the findings of this research, there are some interactions between influential hazard properties. The results imply that it is approximately 23% more likely to observe an accident when encountering a dark small stationary hazard at nighttime like a dark-colored with an observed size of 0.5 m × 0.5 m (e.g., a stone) than a major moving light-colored hazard in the daytime like a camel of 1.5 m ∗ 2 m in size. A green-colored hazard is 27% less likely to involve in an accident at nighttime than hazards with other colors. Each 10 km/h speed increment leads to 1.9% more accident likelihood, and every time the driver encounters a hazard, they will be 0.84% less likely to crash next time.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".