Comparison of Two Negative Binomial Regression Techniques in Developing Accident Prediction Models
Bibliographic record
Abstract
There are several regression techniques to develop accident prediction models. Model development and subsequently the results are affected by the choice of regression technique. The objective of this paper is to compare two types of regression techniques: the traditional negative binomial (TNB) and the modified negative binomial (MNB). The TNB approach assumes that the shape parameter of the negative binomial distribution is fixed for all locations, while the MNB approach assumes that this shape parameter varies with the location's characteristics. The difference between the two approaches in terms of their goodness of fit and the identification and ranking of accident-prone locations is investigated. The study makes use of a sample of accident, volume, and geometric data corresponding to 392 arterial segments in British Columbia, Canada. Both models appear to fit the data well. However, the MNB approach provides a statistically significant improvement in model fit over the TNB approach. A total of 100 locations were identified as accident-prone by both approaches. A comparison between the ranks showed a close agreement in the general trend of ranking between the two models. While the MNB approach appears to fit the data better than the TNB approach, there was little difference in the results of the identification and ranking of accident-prone locations. This is likely due to the nature of the application and the data set used. The difference in results will depend on the extent to which deviant sites exist in the data set.
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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 | 0.028 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".