Vehicle Road Accident Prediction Model along Federal Road FT050 Kluang-A/Hitam-B/Pahat Route Using Excess Zero Data
Bibliographic record
Abstract
Abstract Traffic accidents have become a major socio-economic problem in Malaysia as it is the primary cause of mortality. Over 60 percent of these fatal accidents occurred on rural roads. Nearly half of all fatalities took place on federal roads and over a quarter happened on state roads. It is also estimated that about 2 percent of the country’s Gross Domestic Product (GDP), or approximately RM 9 billion, is lost through road accidents. Previous studies managed to develop several models for modelling the occurrence of accidents, but most of these models have plenty of deficiencies. The following study focuses on stochastic regression models, such as Poisson, Negative Binomial, Zero-Inflated Poisson and Zero-Inflated Negative Binomial with excess zero outcomes on the response variables. Furthermore, in order to specify the regression relationship with a sophisticated result, R-statistical programming is used. The method used is also the updating approach in predicting potential road accidents, which can also produce an accuracy probability of hazardous locations. Based on road accident data collected over a five-year period from 2010 to 2014 at Federal Road F0050: Kluang-A/Hitam- B/Pahat in Johor, Malaysia, results of this study show that Zero Inflated model performed better, in terms of the comparative criteria based on the AIC value.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".