Pedestrian Deaths During the COVID-19 Pandemic
Bibliographic record
Abstract
The COVID pandemic provides a natural experiment examining how a 50-60% reduction in pedestrian activity might lead to a reduction in pedestrian deaths. We assessed whether the reduction in pedestrian deaths was proportional to a one-to-one matching presumed in statistics correlating mobility with fatality. The primary analysis examined New York (largest city in US), and the validation analysis examined Toronto (largest city in Canada). We identified pedestrian activity in each location from the Apple Mobility database, normalized to the baseline in January 2020. We calculated monthly pedestrian deaths from the Vision Zero database in each city with baseline data from 3 prior years. We found a large initial reduction in pedestrian deaths during the lockdown in New York that was transient and not statistically significant during the summer and autumn despite sustained reductions in pedestrian activity. Similarly, we found a large initial reduction in pedestrian deaths during the lockdown in Toronto that was transient and not sustained. Together, these data suggest the substantial reductions in pedestrian activity during the COVID pandemic have no simple correlation with pedestrian fatality counts in the same locations. An awareness of this finding emphasizes the role of unmeasured modifiable individual factors beyond pedestrian infrastructure or other structural contributors.
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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 | 0.001 | 0.001 |
| 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".