Environmental measures to improve pedestrian safety in low- and middle-income countries: a scoping review
Bibliographic record
Abstract
OBJECTIVES: This scoping study aims to identify environmental road safety measures implemented in low- and middle-income countries (LMICs) to reduce pedestrian injuries from collisions with motor vehicles. METHODS: This review followed Arksey and O'Malley's approach and reported results using the PRISMA-SCR 2018 checklist. A literature review was conducted in Medline, Google Scholar, and the Transport Research International Documentation database using keyword-derived medical subject heading terms. A total of 14 articles met the pre-established inclusion criteria and were analyzed using a data extraction matrix. The findings were categorized methodically into three prominent themes: (1) methods for reducing pedestrian exposure, (2) traffic calming strategies, and (3) measures for enhancing pedestrian visibility. RESULTS: Traffic calming strategies, including vehicular speed reduction, roadway contraction, and vertical and horizontal diversionary tactics, emerged as the most effective interventions for reducing pedestrian injuries within LMICs. Conversely, interventions geared towards minimizing pedestrian exposure, such as zebra crossings, crosswalks controlled by traffic signals, underpasses, or overpasses, often produced minimal effects, and occasionally exacerbated the risk of pedestrian accidents. Lack of pedestrian visibility due to density of street vendors and parked vehicles was associated with a higher risk of injuries, while billboards impaired drivers' attention and increased the likelihood of collisions with pedestrians. DISCUSSION: In LMICs, the effectiveness of environmental measures in reducing vehicle-pedestrian crashes varies widely. In the face of resource constraints, implementing interventions for pedestrian safety in LMICs necessitates careful prioritization and consideration of the local context.
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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 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.014 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 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".