Road Transport Safety Improvement Strategy
Bibliographic record
Abstract
The increasing number of road traffic accidents and the high fatality rate in Indonesia, encourage the Government to develop strategies and preventive measures to reduce accidents in the short, medium and long term, although until now the fatality rate is still high. The purpose of this study is to analyze the influence of regulatory factors, humans, traffic signs, supervision and enforcement, and vehicles with technological variables as intervening factors on road safety in an effort to reduce the level of traffic accidents in Indonesia. The location of the study was conducted in 34 provinces in Indonesia, using quantitative data analysis through questionnaires on 500 respondents, with the SEM method (Smart Pls 4.0). The results of the study explain that regulatory factors, humans, traffic signs, supervision and enforcement, and vehicles with technological variables have a positive and significant influence on traffic safety, which means that an increase in the performance of the independent variables has a positive impact on traffic safety. The recommendations from this study are the priority of traffic safety improvement policies, namely: increasing active and passive safety in vehicles, using safety technology in vehicles, utilizing CCTV and e-ticketing for supervision and enforcement, fulfilling ffective and efficient traffic signs and safety campaigns for road users and improving regulations.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.026 | 0.007 |
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".