Road Traffic Injuries, Trends, and Patterns: A Five-Year Retrospective Analysis Using Secondary Police Data in Nepal
Bibliographic record
Abstract
Introduction: Road traffic accidents are a major public health concern in Nepal, causing significant morbidity and mortality. The study goal was to determine the trends of road traffic injuries in Nepal from Fiscal Years 2020/21 to 2024/25 (mid-July 2020 - mid-July 2025). Methods: A descriptive retrospective study was conducted to analyze de-identified and pooled road traffic accident records from Nepal, following receipt of ethical clearance from the Nepal Health Research Council (ERB no. 607_2025). Data were analyzed using Microsoft Excel 2019. Results: The findings show that road traffic accidents have shown an apparent spike from FY 2020/21 to 2024/25, with both vehicle collisions and accident incidences ascended significantly. Reported vehicle crashes rose from 33135 in FY 2020/21 to 43165 in FY 2024/25, while total RTAs increased from 20640 to 28692 over the same period. RTAs surged in six provinces of Nepal. Two-wheelers, four-wheelers, and public transport vehicles accounted for the majority of incidents. Speeding 46398 (44.08%), mechanical failure 1173 (39.14%), potholes 685 (21.69%), pedestrian road crossings 5523 (79.55%), and unfavourable weather conditions like fog and mist 122 (26.23%) were major contributing factors. Although injuries increased significantly, fatality rates did not rise in same proportion. Conclusions: The results show that road traffic accidents are becoming more common in Nepal, especially involving motorbikes, four-wheelers, and public vehicles. Reducing accident rates and their effects may require increased road safety enforcement, better infrastructure, and awareness-raising initiatives.
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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) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".