Does Automated Enforcement Presence Impact Collisions and Crime?
Bibliographic record
Abstract
Enforcement agencies generally operate under a strict budget and with limited resources. For this reason, they are continually searching for new approaches to maximize the efficiency and effectiveness of their deployment. The Data-Driven Approaches to Crime and Traffic Safety approach attempts to identify opportunities where increased visibility of traffic enforcement can lead to a reduction in collision frequencies as well as criminal incidents. Previous research developed functions to model collisions and crime separately, despite evidence suggesting that the two events could be correlated. Additionally, there is little knowledge of the implications of automated enforcement programs on crime. This study developed a Multivariate Poisson-Lognormal model for the city of Edmonton to quantify the correlation between collisions and crime and to determine whether automated enforcement programs can also reduce crime within a neighborhood. The results of this study found a high correlation between collisions and crime of 0.72 which indicates that collision hotspots were also likely to be crime hotspots. The results of this paper also showed that increased enforcement presence resulted in reductions not only in collisions but also in crime. If a single deployment can achieve multiple objectives (e.g., reducing crime and collisions), then optimizing an agency’s deployment strategy would decrease the demand on their resources and allow them to achieve more with less.
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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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".