Riders Who Avoided Public Transit During COVID-19
Bibliographic record
Abstract
Millions of North Americans stopped riding public transit in response to COVID-19. We treat this crisis as a natural experiment to illustrate the importance of public transit in riders’ abilities to access essential destinations. We measured the impacts of riders forgoing transit through a survey of transportation barriers completed by more than 4,000 transit riders in Toronto and Vancouver (Canada). We used Heckman selection models to predict six dimensions of transport disadvantage and transport-related social exclusions captured in our survey. We then complemented model results with an analysis of survey comments describing barriers that individuals faced. Lack of access to alternative modes is the strongest predictor of a former rider experiencing transport disadvantage, particularly neighborhood walkability and vehicle ownership. Groups at risk of transport disadvantage before COVID-19, particularly women and people in poorer health, were also more likely to report difficulties while avoiding public transit. Barriers described by respondents included former supports no longer offering rides, gendered household car use dynamics, and lack of culturally specific or specialized amenities within walking distance. Policymakers should plan for a level of redundancy in transportation systems that enables residents to access essential destinations when unexpected service losses occur. Designing communities that enable residents to walk to those essential destinations will help reduce the burdens faced by transit riders during crises that render transit unfeasible. At the same time, planners championing active travel as an alternative to transit during such crises also need to devise solutions for former transit riders for whom active travel is ill suited, for example, due to physical challenges with carrying groceries or needing to chaperone children.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".