Modeling of the Effect of Toll Road Characteristics on Accident Rate
Bibliographic record
Abstract
A traffic accident is an event on the road that is unexpected and unintentional.Traffic accidents have an impact on the national economy as evidenced by the contribution of traffic accidents to economic losses in the form of a decrease in GDP by 2.9% -3.1% or equivalent to USD 1.332 Billion -USD 1.465 Billion.So, to minimize accidents, an analysis is needed that is useful for anticipating accidents.This study intends to obtain the effect of the characteristics of intercity toll roads, as well as urban toll roads, on the accident rate.The stages conducted in this research are previous research studies, data collection, determining variables, and modeling.Based on the analysis that has been done in this study, a negative binomial distribution is used to determine the accident rate of inter-city toll roads and the Poisson distribution to determine the accident rate of toll roads within the city and the fatality rate of toll roads between cities and within cities.There are nine parameters used in this model, which can cover more things than similar studies that have been done before.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".