Switching back to transit? Post-pandemic commute choices and emotional response
Bibliographic record
Abstract
• 75% of pre-pandemic PT users maintained or returned to transit post-pandemic. • Transit proximity strongly associated with post-pandemic PT use patterns. • Emotional stress and captivity were associated with transit use decisions. • Permanent car users were younger, lived farther, and reported high pandemic stress. • Policies should address positive and negative emotions, alongside PT reliability. During the COVID-19 pandemic, public transit (PT) systems faced major disruptions and heightened safety concerns. While some pre-pandemic PT commuters shifted to telecommuting, others continued using PT or switched to cars. This study adopts a quasi-longitudinal approach to examine the socio-demographic and emotional factors associated with commute mode choices before, during, and after the pandemic among essential workers who could not telecommute. Using a case study design and retrospective survey data from 350 employees at the McGill University Health Centre in Montreal, Canada, we analyze three groups of pre-pandemic PT users: consistent PT users (PT-PT-PT), temporary car users who returned to PT (PT-Car-PT), and permanent car switchers (PT-Car-Car). Multinomial logistic regression results show strong correlations between post-pandemic commuting patterns and transit proximity, employment type, and cultural background. Individuals living within 2 km of a metro or train station, older workers, and those in administrative or professional roles were more likely to remain transit users. Conversely, younger individuals, nurses, and those working evening or night shifts were more likely to shift to car use. Commute-related emotions also varied across groups. Consistent PT users reported less variation in safety concerns and commuting stress, likely reflecting the comfort of habitual use. PT-Car-PT users expressed a stronger sense of captivity after returning to transit, while PT-Car-Car users reported lingering stress that, combined with practical factors such as bedside work, off-peak schedules, and vehicle access, reinforced their continued reliance on cars. Improving service reliability, comfort, and perceived safety is essential to enhance positive emotional responses and support long-term transit loyalty.
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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 | 0.007 | 0.002 |
| 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 | 0.001 |
| 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".