Public transport and the COVID-19 pandemic: A comparative analysis of trends and policies in Great Britain, Germany, the USA, Canada, and Australia
Bibliographic record
Abstract
This paper compares changes in urban public transport (PT) demand and supply before, during, and after COVID-19 in Great Britain, Germany, the USA, Canada, and Australia. We also examine a range of PT system measures and government policies implemented during and since the pandemic to improve safety, adjust service levels, and encourage ridership. Ridership fell sharply in 2020 and 2021, when COVID-19 rates were highest. As a percentage of 2019 levels, the lowest annual ridership for each country was 31% for Great Britain, 42% for Canada, 46% for the USA, 48% for Australia, and 64% in Germany. The latest full year of available data (2024) indicates that Germany (94%), Great Britain (90%), and Australia (90%) recovered the highest percentages of 2019 ridership levels, compared to 83% in Canada and 77% in the USA. Bus ridership declined less than rail ridership and recovered more fully, especially in the USA, Canada, and Australia. Our analysis of PT in five large cities finds that recovery rates were generally higher on weekends than on weekdays, both for bus and rail. The most important government policy for PT has been a massive increase in funding, especially from central governments, to offset the large operating budget deficits resulting from lost passenger revenue. That funding enabled PT systems to maintain or reduce fares while avoiding large reductions in supply. Dependable government support will be necessary in the coming years to make PT financially sustainable and to enable long-term planning for infrastructure modernization and improved service.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.000 | 0.001 |
| 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".