129 Effectiveness of automated speed enforcement in reducing vehicle speeds within school community safety zones in Toronto, Canada
Bibliographic record
Abstract
Background Speed is one of the most important determinants of traffic collisions and the severity of resulting injuries. While previous studies have demonstrated the effectiveness of Automated Speed Enforcement (ASE) on major roads in reducing speed violations and related incidents, research exploring its impact on residential and community areas is limited. In 2020, the City of Toronto introduced an ASE program with 50 mobile camera units, which were relocated to new school areas at the end of each phase. This study analyzes data collected from 250 locations during the initial five phases of the ASE program. Objective This study aims to evaluate the impact of ASE cameras on reducing vehicle speeds near schools in Toronto. Methods Pre-ASE installation data, encompassing speed and vehicle volume metrics collected post-2018 using pneumatic road tubes, were compared with data obtained during the ASE deployment. Our analysis examined the proportion of vehicles exceeding speed limits and changes in the 85th percentile speeds. Fixed-effects regressions with robust standard errors using generalized estimating equation (GEE) was employed to estimate mean differences in 85th percentile speeds over time, accounting for program phases, seasonality, and the built environment. The relative risk (RR) of vehicles exceeding speed limits during the ASE intervention, adjusting for confounding factors was also determined. Results Our findings reveal a large reduction in the 85th percentile speed by 11 km/h and a 46% decrease in the risk of vehicles exceeding speed limits during the ASE intervention. This reduction was particularly notable on roads with higher speed limits. Some locations in inner suburban Toronto near schools continued to experience high-speed traffic even with the cameras, which poses potential risks to vulnerable road users. Conclusions The Toronto ASE program has effectively reduced speeding. In some areas, speeding above 30 km/h (WHO recommended speed for busy mixed-traffic urban areas) was prevalent even with the ASE intervention period due to set speed limits and connectivity to high-volume arterial roads and highways. Our results emphasize the potential of ASE as an important component of Toronto’s Vision Zero initiatives, alongside other speed management strategies, in enhancing safety near schools.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".