Revisiting Variability of Dispersion Parameter of Safety Performance for Two-Lane Rural Roads
Bibliographic record
Abstract
Safety performance functions (SPFs) are commonly calibrated with negative binomial regression in which a dispersion parameter that represents extra-Poisson variation is estimated. The primary use of this parameter is in empirical Bayes estimation for safety management applications such as treatment evaluation and network screening. It stands to reason that the importance of precise estimation of the dispersion parameter should be established. Recent research has suggested that the dispersion parameter is not constant but actually varies from site to site, depending on site characteristics such as segment length. In revisiting the dispersion parameter issue in this empirical investigation, previous research on this issue is reinforced by filling a number of knowledge gaps. First, light is cast on the dispersion parameter variation for SPFs for two-lane rural roads, an important entity type for which there is little or no knowledge in this regard. In this study, more precise model forms are investigated to represent the variation with respect to the key variable, segment length. This investigation confirms that the dispersion parameter is inversely related to segment length but reveals that it is not inversely proportional to segment length, as suggested in other research. Second, it was found that the dispersion parameter is smaller and the variation less pronounced with better-specified models. Finally, additional evidence is provided to suggest that dispersion parameter variation matters more for shorter segment lengths.
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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 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".