Safety Impact of Automated Speed Camera Enforcement: Empirical Findings Based on Chicago’s Speed Cameras
Bibliographic record
Abstract
Speeding was a factor in over a quarter of crash fatalities annually in the U.S. from 2009 to 2018. In some cities, automated speed camera enforcement is being used to curb speeding and improve roadway safety in instrumented areas. This paper reviews the effectiveness of automated speed cameras and some of the considerations informing the public debate around them in the United States. It then employs data from the City of Chicago, Illinois and the empirical Bayes approach to examine how effective speed cameras have been at reducing injury crashes and fatalities. Chicago installed most of its currently operating speed cameras in the 2013 to 2014 period. From 2015 to 2017, we estimate a 12% reduction in fatal and injury crashes across treated locations included in our analysis. Fatality and severe injury crashes declined by 15%. Some treated sites did not see the expected safety benefits, however. Recommendations to improve efficacy are made.
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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 | 0.005 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".