Analysis of Traffic Violations Among Novice Drivers and Motorcyclists in Ecuador: A Road Safety Perspective
Bibliographic record
Abstract
This study focuses on analyzing traffic violations committed by novice drivers of non-professional licenses in Ecuador.Despite the extensive literature on the relationship between driver age, experience, and road accidents, traffic violations, as indicators of risky behaviors, have received limited attention.The aim of this research is to provide a detailed insight into the trends and characteristics of violations committed by novice drivers and motorcyclists.The sample was collected between 2016 and 2020 from a driving school in Loja, Ecuador.Demographic variables such as age, education level, and gender were analyzed in relation to the violations committed.The results revealed significant gender differences, with a higher proportion of violations committed by males.Additionally, an increase in violations during the first years after obtaining the license, followed by a gradual decrease, was observed.The most common violations included not wearing a seatbelt and parking in prohibited areas.Furthermore, differences in violation trends between drivers and motorcyclists were identified, suggesting the need for differentiated training strategies for both groups.Regarding the relationship between students' age and violations, it was found that younger drivers and motorcyclists were more likely to commit violations, which could be attributed to a penchant for sensation-seeking among young individuals.This study provides a solid foundation for revising and improving training programs in driving schools in Ecuador.A differentiated approach by gender and age group is suggested, as well as the implementation of active learning methods and self-reflection in road safety education.All of these actions will contribute to improving road safety in Ecuador.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.002 | 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".