Use of Objective Safety Evidence to Deploy Automated Enforcement Resources
Bibliographic record
Abstract
Automated enforcement programs have been an important tool for improving traffic safety. Previous work provides strong evidence supporting the impact that these programs have on increasing safety either on a micro-level (e.g., road segments), or at a macro-level (e.g., neighborhood, city). In both cases, there are many variables that can influence and affect the safety impacts of the enforcement program. Additionally, there is a lack of understanding of how specific deployment parameters (e.g., how often to visit an enforcement site) can influence the overall safety on a macro-level (e.g., traffic analysis zone). The objective of this study is to quantify the impact that automated enforcement has on collisions on a macro-level as well as to develop models that would provide enforcement authorities with an empirical tool to help plan their deployment strategy. The results show that an increase in the number of tickets issued for exceeding the speed limit resulted in a decrease in collisions, for all collision severities. Moreover, the results also showed that collision reductions were also associated with spending a longer time enforcing a site for each visit. Quantifying these safety impacts supports decision makers by providing them with an opportunity to analyze the safety benefits in relation to their deployment strategy to maximize the efficiency of their resources.
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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 | 0.020 | 0.150 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".