Modeling traffic crash rates of road segments through a lognormal hurdle framework with flexible scale parameter
Bibliographic record
Abstract
Summary This study examines distributional characteristics of crash rates for road segments using observed accident data. The results indicate that the distribution of crash rates is mixed and right‐skewed, which motivates the consideration of non‐normal distributions. With the aid of Kolmogorov–Smirnov tests, kernel density plots, and Q–Q plots, the lognormal distribution is verified as an appropriate candidate for representing the positive domain of crash rates. Then, a lognormal hurdle model was developed and also compared with gamma and Weibull hurdle models. Further, the lognormal hurdle model was revised by allowing the scale parameter to vary with respect to explanatory variables. Such a modification enables the heterogeneous skewness of samples to be captured while enhancing the modeling flexibility. The proposed model was also compared with a Tobit model, an alternative approach that treats crash rates as censored data. Among all these models, the proposed lognormal hurdle model with flexible scale parameter presents the best modeling performance, and the analyses also reveal that several explanatory variables affect crash rates through not only the location parameter but also the scale parameter in the lognormal model. This study finally attempted to inspect crash rates through count models, and it discovered that the proposed hurdle model is superior because it is able to output the whole distribution form of crash rates, whereas the crash count model can only provide the expected value of crash rates, provided the exposure variable servers as an offset term in the link function of the mean parameter. Copyright © 2015 John Wiley & Sons, Ltd.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 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".