Bus Drivers’ Perspectives on Factors Contributing to Road Traffic RTAs on Prithvi and Mugling-Narayanghat Highway Segment in Nepal
Bibliographic record
Abstract
Road traffic accidents (RTAs) remain a leading cause of mortality and injury worldwide, with low-and middle-income countries disproportionately affected despite having a smaller share of the global vehicle fleet.This study investigates the factors contributing to RTAs on Nepal's Prithvi Highway and the Mugling-Narayanghat road segment, with a specific focus on driver behavior, infrastructure deficiencies, environmental conditions, and vehicle-related issues.Data were collected through a structured questionnaire survey from 210 bus drivers who regularly operate along these routes.The reliability of the instrument was confirmed using Cronbach's alpha, and data were analyzed using the Relative Importance Index (RII) to rank the severity of contributing factors and one-way ANOVA to examine differences across demographic groups.Results reveal that human factors, particularly speeding, drunk driving, and driver distractions, are the most significant contributors to accidents, followed by infrastructure factors such as narrow roads and inadequate signage, adverse weather conditions like rain and fog, and vehiclerelated issues, including defective tires.ANOVA results indicate significant variations in accident-related risk perceptions based on drivers' age, education level, and years of experience, with younger drivers more prone to distraction and older drivers more affected by poor road conditions.This study provides novel empirical evidence from the perspective of road users, offering practical insights for developing targeted road safety interventions.Recommendations include enhanced driver training, road infrastructure upgrades, regular vehicle maintenance, and awareness campaigns to for reducing RTAs.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".