Causes of Traffic Accidents: Implication to an Effective Traffic Management
Bibliographic record
Abstract
The study sought to assess the causes of traffic accidents and its implication to an effective traffic management in Cagayan de Oro City. The independent variables were limited to equipment, roadway design, poor road maintenance, driver behavior, speed, improper loading, and unloading. The dependent variables narrowed to traffic incidents in Cagayan de Oro City. Data from the Police Traffic Unit is used to serve as respondents of the study. The data reveals that the majority of the causes of the accident were human error in a total of 4,370 cases for the year of 2014. Also human error in a total of 5,275 cases for the year of 2015, and still human error is the cause of accident for the first quarter of 2016 which have 1,796 cases. For the involvement of the collision, the data shows the highest case reported were in the car had 1,845 reports in the year of 2014. In 2015, the car had the greatest case reported in the involvement of the accident which had the same cases reported as 2014. And in the first quarter of 2016, a private vehicle had 1,349 cases reported for the involvement of accident in Cagayan de Oro City. For the common causes of the traffic accident, the data revealed that majority were in daytime visibility which had 4,492 in 2014, 5,536 in 2015 and 1,814 in the first quarter of 2016. For road condition, the majority of accident occurred were in the concrete of 2014 which had 4,492 cases, slippery in 2015 which had 5, 336 cases and the concrete in the first quarter of 2016 which had 1,335 cases reported. For weather condition, the majority were at a fair in 2014 which had 4,492 cases. In 2015 rainy were the dominant action which had 3, 453 cases and in the first quarter of 2016 fair condition which had 1,808 cases. For causes of traffic accident 4,370 cases for the year of 2014, also the human error in a total of 5,275 cases for the year of 2015, and still human error is the cause of the accident for the first quarter of 2016 which have 1,796 cases. For the places of the accident, the data revealed that Barangay Bulua is the most prone area of the accident which had 246 cases for the year of 2014. In 2015 data did not indicate where was the most prone area of an accident and for the first quarter of 2016, Barangay Bulua is the most prone area of an accident which had 61 cases reported K e ywords: Philippine National Police, Traffic Management, Ordinance, Law.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".