Frequent public transit users views and attitudes toward cycling in Canada in the context of the COVID-19 pandemic
Bibliographic record
Abstract
Several Canadian cities observed a shift from public transit use to private cars and active transport modes during the COVID-19 pandemic. At a moment where pre-pandemic public transit users are reconsidering their travel options, studies describing their attitudes toward cycling are lacking. Because most trips in urban areas involve short- and mid-range travel, cycling is seen as a promising environmentally sustainable means of transportation. This study aims to describe how pre-pandemic public transit users in Toronto and Vancouver view cycling, including their comfort with available infrastructure, cycling frequency, and perceived barriers to adoption. Data from the Public Transit and COVID-19 Survey, a web-based panel survey of over 3,500 regular transit riders in Toronto and Vancouver administered in May 2020 and April 2021 were analysed. Applying Geller's typology, 70% of participants could be classified as interested but concerned and one fifth as no way no how regarding their comfort levels toward cycling. Women were more likely to be no way no how cyclist type. Weather, lack of safe routes, and having to carry things were the main barriers to cycling in both cities. Our results give insight on who should be targeted by city initiatives aiming to promote changes toward more active modes of transportation. Further studies with a causal design are required to identify possible mitigating strategies to the main barriers to cycling.
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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 | 0.003 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".