Spatial Effects on Zone-Level Collision Prediction Models
Bibliographic record
Abstract
A recent study developed a set of zone-level negative binomial collision prediction models to investigate the relationship between various transportation and sociodemographic characteristics and overall roadway safety. The developed models used data from Metro Vancouver, British Columbia, Canada, and considered the effect of Poisson variations and heterogeneity (extra variation) on collision occurrence. This study aims to evaluate spatial effects on the occurrence of collisions and to check whether the inclusion of spatial variables can improve the goodness of fit and inference capability of those previously developed prediction models. Transit-reliant and application-based collision prediction models with spatial correlations were developed by using the WinBUGS software. The convergence of the developed models was tested by trace plots of the parameter estimated, the Brooks–Gelman–Rubin statistics, and ratios of Monte Carlo errors relative to the standard deviations of the estimates. The results showed that incorporation of the spatial correlations affected the parameter estimates, the values of dispersion parameters and intercepts, and also the t-statistics. The effect of the main exposure variable on all of the models for total, severe, and property-damage-only collisions was found to be smaller under spatial models. The smaller values of the exponents of the main exposure variable confirmed the assumption that spatial effects need to be considered in collision prediction models to mitigate any potential bias associated with model misspecification.
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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 | 0.017 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.004 | 0.003 |
| 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".