Predicting Wet-Road Crashes Using the Finite-Mixture Zero-Truncated Negative Binomial Model
Bibliographic record
Abstract
Inclement weather affects traffic safety in various ways. Crashes on rainy days not only cause fatalities and injuries but also significantly increase travel time. Accurately predicting crash risk under inclement weather conditions is helpful and informative to both roadway agencies and roadway users. Safety researchers have proposed various analytic methods to predict crashes. However, most of them require complete roadway inventory, traffic, and crash data. Data incompleteness is a challenge in many developing countries. It is common that safety researchers only have access to data on sites where a crash has occurred (i.e., zero-truncated data). The conventional crash models are not applicable to zero-truncated safety data. This paper proposes a finite-mixture zero-truncated negative binomial (FMZTNB) model structure. The model is applied to three-year wet-road crash data on 395 divided roadway segments (total 586 km), and the parameters are estimated using the Markov chain Monte Carlo (MCMC) method. Comparison indicates that the proposed FMZTNB model has better fitting performance and is more accurate in predicting the number of wet-road crashes. The model is capable of capturing the heterogeneity within the sample crash data. In addition, lane width showed mixed effects in different components on wet-road crashes, which are not observed in conventional modeling approaches. Practitioners are encouraged to consider the finite-mixture zero-truncated modeling approach when complete safety dataset is not available.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".