Assessing the effect of automated speed enforcement and comprehensive measures on road safety in Rwanda
Bibliographic record
Abstract
OBJECTIVES: Daily, approximately 3,400 traffic-related deaths occur globally, with over 90% concentrated in low and middle-income countries (LMICs). Notably, Rwanda has one of the highest road traffic death rates in the world (29.7 per 100,000 people) and is the first low-income country to implement a national Automated Speed Enforcement (ASE) policy. The primary goal of this study is to evaluate the effectiveness of ASE cameras in reducing the primary outcome of road traffic deaths and secondary outcomes of serious injury crashes and fatal crashes. METHODS: The study used data on road traffic deaths, and serious injury and fatal crashes collected by the Rwanda National Police between 2010 and 2022. Interrupted time series (ITS) models were fit to quantify the association between ASE and change in road traffic crash outcomes, adjusted for COVID-19-related variables (such as the start of the pandemic, the closure of schools and bars), along with exposure variables (such as GDP and population), and other concurrent road safety measures (such as road safety campaigns). RESULTS: The ITS models show that the implementation of ASE cameras significantly reduced road traffic deaths, serious injury crashes, and fatal crashes at the provincial level. For instance, the implementation of ASE cameras in the whole of Rwanda in April 2021 was significantly associated with a 0.14 (95% CI [0.072, 0.212]) reduction in monthly death incidence, equating to a 38.16% monthly decrease compared to the period before their installation (January 2010-March 2021). CONCLUSION: This study emphasizes the significant association of ASE in Rwanda with improved road traffic crash outcomes, a result that may inform road safety policy in other LMICs. Rwanda has become the first low-income country to implement nationwide scaling of ASE in Africa, paving the way for the generation of valuable evidence on speed-related interventions. In addition to new knowledge generation, African road safety research efforts like this one are opportunities to grow academic and law enforcement cooperations while improving data systems and sources for future research benefits.
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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 | 0.009 | 0.021 |
| 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 | 0.000 | 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".