Road traffic crashes trends of severity and injuries in Osun state, Nigeria
Bibliographic record
Abstract
Road traffic accidents (RTA) have been of concern both in developed and developing nations of the world. This study examined the road accident trends in Osun State, Nigeria, over 7 years (2015 to 2021). The dataset used is secondary data obtained from the Federal Road Safety Commission (FRSC), Osun State, Nigeria. This includes the number of cases (fatal, serious, and minor), the number of people dead, and the number of people injured. Multivariate Time Series analysis shows that an average of 243 and 44 people were injured and killed, respectively, quarterly. For the total of 1859 reported cases, 6809 and 1232 people were injured and killed, respectively, for the 7 years. The Vector Autoregressive (VAR) model was applied to study the underlying patterns and forecast future values for the number of deaths and injuries for the year 2022. Based on the value of Akaike's Information Criteria (AIC) and Bayesian's Information Criteria (BIC), the model with the constant was the best of all. The result of the forecast indicates that 245 people are predicted to be injured in the first quarter of 2022 and 243 people in the second, third, and fourth quarters. 44 people are predicted to be killed in the first quarter of 2022 and 45 people in the second, third, and fourth quarters, accident occurrence for the 7 years has a low severity index. According to the findings, it was discovered that the number of people injured and killed in traffic accidents has been rising over time. This is the outcome of several variables, including environmental, human, and road-related factors. Therefore, to decrease traffic-related deaths and injuries, the government and concerned non-governmental organizations must embark on educating drivers about defensive driving, enforcing road traffic laws, and traffic education to the populace.
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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 | 0.000 | 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 | 0.000 |
| 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".