Cycling through the COVID-19 Pandemic to a More Sustainable Transport Future: Evidence from Case Studies of 14 Large Bicycle-Friendly Cities in Europe and North America
Bibliographic record
Abstract
This article examines the impact of COVID-19 on cycling levels and government policies toward cycling over the period 2019 to 2021. We analyze national aggregate data from automatic bicycle counters for 13 countries in Europe and North America to determine month-by-month and year-to-year changes in cycling levels in 2020 and 2021 compared to 2019. That aggregate analysis is complemented by case studies of 14 cities in the USA, Canada, the UK, Belgium, France, Spain, and Germany. Although there was much variation over time, among countries, and among cities, cycling levels generally increased from 2019 to 2021, mainly due to growth in cycling for recreation and exercise. In contrast, daily trips to work and education declined. All 14 of the cities we examined in the case studies reported large increases in government support of cycling, both in funding as well as in infrastructure. Bikeway networks were expanded and improved, usually with protected cycling facilities that separate cyclists from motorized traffic. Other pro-cycling measures included restrictions on motor vehicles, such as reducing speed limits, excluding through traffic from residential neighborhoods, banning car access to some streets, and re-allocating roadway space to bicycles. Car-restrictive measures became politically possible due to the COVID-19 crisis.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".