Facing the future of transit ridership: shifting attitudes towards public transit and auto ownership among transit riders during COVID-19
Bibliographic record
Abstract
Public transit agencies face a transformed landscape of rider demand and political support as the COVID-19 pandemic recedes. We explore people's motivations for returning to or avoiding public transit a year into the pandemic. We draw on a March 2021 follow-up survey of over 1,900 people who rode transit regularly prior to the COVID-19 pandemic in Toronto and Vancouver, Canada, and who took part in a prior survey on the topic in May 2020. We investigate how transit demand changes associated with the pandemic relate to changes in automobile ownership and its desirability. We find that pre-COVID frequent transit users between the ages of 18-29, a part of the so-called "Gen Z," and recent immigrants are more attracted to driving due to the pandemic, with the latter group more likely to have actually purchased a vehicle. Getting COVID-19 or living with someone who did is also a strong and positive predictor of buying a car and anticipating less transit use after the pandemic. Our results suggest that COVID-19 may have increased the attractiveness of auto ownership among transit riders likely to eventually purchase cars anyway (immigrants, twentysomethings), at least in the North American context. We also conclude that getting COVID-19 or living with someone who did is a positive predictor of having bought a car. Future research should consider how having COVID-19 transformed some travelers' views, values, and behaviour. Supplementary Information: The online version contains supplementary material available at 10.1007/s11116-022-10344-2.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".