Evaluating the impact of a School Streets intervention on personal vehicle use and air quality: Pilot studies at four suburban schools in Canada
Bibliographic record
Abstract
Active School Transportation (AST) has declined globally, coinciding with increased personal vehicle use. School Streets interventions aim to reverse this trend by temporarily closing streets adjacent to schools during drop-off and pick-up times to promote AST and reduce vehicle use while offering additional benefits like improved air quality. While grey literature suggests effectiveness, rigorous evaluations are lacking. This study examined School Streets at four suburban schools in two Canadian cities, where implementation strategies differed. Changes in personal vehicle use were assessed through vehicle counts, while traffic, emissions, and dispersion models estimated fluctuations in vehicle emissions and related ambient air pollution. Results showed School Streets reduced personal vehicle use by 35 %, vehicle emissions by 31 %, and related ambient air pollution by 93 %. However, post-intervention reductions fell to 5 %, suggesting that the benefits are primarily constrained to the days when School Streets are active. Effectiveness varied by implementation strategy. In the city where a cross-disciplinary team was involved, benefits were greater, suggesting broader stakeholder engagement may enhance impact. In the city where the school board led implementation, effects were more sustained, highlighting the value of school-driven leadership. Afternoon-only interventions were less effective in the morning but equally or more effective in the afternoon. Closing only drop-off and pick-up areas was less impactful than restricting a larger street section. Morning reductions were larger, likely due to higher initial vehicle volumes, while afternoon reductions were more sustainable, likely reflecting students’ increased likelihood of walking home. These findings highlight School Streets' potential to reduce vehicle use and emissions, emphasizing the need for strategic implementation and stakeholder involvement. • School Streets reduced vehicle use by 35 %, vehicle emissions by 31 %, and related ambient air pollution by 93 %. • Post-intervention reductions in vehicle use, emissions, and air pollution were more modest (5 %). • Greater benefits were observed with cross-disciplinary teams, highlighting the value of broad stakeholder involvement. • Interventions led by school boards showed more sustained effects, emphasizing the importance of school-driven leadership. • Morning reductions in vehicle use were larger, while afternoon reductions were more sustainable.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".