Before-and-After Empirical Bayes Evaluation of Automated Mobile Speed Enforcement on Urban Arterial Roads
Bibliographic record
Abstract
Speeding is a leading factor in road collisions and is found to contribute to approximately one-third of all fatal collisions. Speed enforcement is one of the most common countermeasures used to reduce speed. However, a gap exists in the literature regarding the effectiveness of automated mobile photo enforcement on urban arterial roads. This study addresses this gap using the before-and-after empirical Bayes method to account for regression to the mean effects and other confounding factors. Locally developed safety performance functions and yearly calibration factors for different collision severities were obtained by using a reference group of urban arterial roads. The evaluation period covers 8 years. Collision records, deployment information, traffic counts, and road geometric data were collected. The results showed consistent reductions in different collision severities; the reductions ranged from 14% to 20%, with the highest reductions observed for severe collisions. The enforced segments were further categorized according to site selection criteria and deployment hours to examine the effect of enforcement on collision reduction. More reductions were found at segments that had more collisions during the before period and longer deployment hours. The study also compared the safety effects of continuous and discontinuous enforcement strategies on different arterials, and the analysis revealed that continuous enforcement achieved more reductions across all severities and types of collisions. The study also investigated the spillover effects on adjacent unenforced approaches. Significant reductions were found, and these results are discussed with regard to the general and specific deterrence of the enforcement.
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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 | 0.027 | 0.073 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".