The growing gap in pedestrian and cyclist fatality rates between the United States and the United Kingdom, Germany, Denmark, and the Netherlands, 1990–2018
Bibliographic record
Abstract
Using official national data for each country, this article calculates trends in walking and cycling fatalities per capita and per km in the USA, the UK, Germany, the Netherlands, and Denmark. From 1990 to 2018, pedestrian fatalities per capita fell by 23% in the USA vs. 66%–80% in the other countries; cyclist fatalities per capita fell by 22% in the USA vs. 55%–68% in the other countries. In 2018, pedestrian fatality rates per km in the USA were 5–10 times higher than in the other four countries; cyclist fatality rates per km in the USA were 4–7 times higher. The gap in walking and cycling fatality rates between the USA and the other countries increased over the entire 28-year period, but especially from 2010 to 2018. Over that 8-year period, per-capita fatality rates in the USA rose by 19% for pedestrians and 11% for cyclists; per-km fatality rates rose by 17% for pedestrians and 33% for cyclists. By comparison, fatality rates either fell or remained stable in the four European countries. We reviewed the relevant literature to identify factors that might help explain the much lower walking and cycling fatality rates in Europe compared to the USA. Possible explanatory factors include better walking and cycling infrastructure; lower urban speed limits; fewer vehicle km travelled; smaller and less powerful personal motor vehicles; and better traffic training, testing, and enforcement of traffic regulations. We recommend that the USA consider implementing an integrated package of mutually reinforcing safety measures such as those that have been successfully implemented in the Netherlands, Denmark, and Germany to reduce pedestrian and cyclist fatality rates.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".