Time Series Analysis of Road Traffic Accidents in Bauchi State
Bibliographic record
Abstract
Road traffic accidents rate in the recent days is becoming alarming in the country given the human and technological advancement. Lot of lives are being wasted pathetically, premature deaths and destruction of scarce human and materials resources. Nigeria has a huge natural and human resources who are day by day wasted and destroyed by this controllable disaster. However, this work is based on the statistical analysis of road traffic accidents and ways of reducing it to its bearest minimum. The secondary data used in this research was collected from the Federal Road Safety Commission (FRSC) RS 12.1. Trend shows that road accidents are increasing in Bauchi and that it is highest between October and December. Time series (ARIMA) is the method used with model (1,1,1) having the least Akaike Information Criteria (AIC) selected as the best model in analyzing the collected data which clearly shows that there is an increase in road accidents over the course of the years in each quarter. Furthermore, the selected model was used to forecast four (4) years which also shows an increase. Various recommendations were made based on the findings of this work which include proper funding and provision of up to date equipment
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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 | 0.004 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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".