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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".