Forecasting Road Traffic Accidents in Metro Manila Using ARIMA Modeling
Bibliographic record
Abstract
In this paper, we have determined and analyzed the behavior of road traffic accidents (RTAs) in Metro Manila, Philippines over the period of 2012-2021, and created a forecast for the next 5 years using ARIMA modeling. This study used 10-year historical monthly data collated through the Metro Manila Accident Recording and Analysis System program for the years 2012 through 2021. Our result suggests that the total RTAs in Metro Manila gradually increased until the first quarter of 2020, then it plummeted and reached its lowest point in April 2020 due to COVID-19 lockdown. As lockdown eases, it bounced back but only halfway. Similarly, RTAs resulting in damage to property also bounced back to only halfway as lockdown eases, while RTAs resulting in injuries (fatal and nonfatal) bounced back to their normal range as before the lockdown. Despite the decrease in total RTAs, the ratio of RTAs resulting in injuries drastically increased during the lockdown due to reckless driving behaviors. Using Box-Jenkins methodology of ARIMA modeling, this study identified ARIMA (1, 1, 12) as the best model. With this model, the forecast shows that the total RTAs will stay halfway in 2022 and gradually decrease for the next 4 years.
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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 | 0.002 | 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".