Negative Binomial Regression Model for Road Accident Analysis in Hong Kong
Bibliographic record
Abstract
This study evaluates the influence of traffic volume on accident frequency and investigates other possible factors that contribute to accident risk, using vehicle kilometers (VKM) as a proxy of exposure. Information on traffic volume and road design factors was obtained from the Hong Kong Annual Traffic Census (ATC), and accident data were extracted from the Hong Kong Accident Database System (TRADS) respectively. These data were incorporated into a road network map of Hong Kong using the Geographical Information System (GIS). Count data models were employed to the analysis of accident frequency. As the data are subject to over-dispersion, a negative binomial regression method was deployed to measure the association between accident frequency and traffic volume and to control for the effects of factors such as temporal variation and road environment. The results indicate that greater traffic volume leads to a less than proportionate increase (a coefficient estimate of 0.62) in accident frequency and thus a decrease of accident risk. The interaction effects by traffic volume and other possible factors on accident occurrence are also revealed.
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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 | 0.009 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.040 | 0.006 |
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".