The Effect of the COVID-19 Pandemic on Transit Mode Choice in Calgary, Alberta
Bibliographic record
Abstract
The COVID-19 pandemic has reduced travel demand globally across all modes. Public transit ridership has been especially affected, as COVID-19 has reduced the attractiveness of transit compared to unshared transportation modes. Transit agencies worldwide have reduced service in response to lost fare revenue and reduced ridership. To recover from the pandemic and remain a viable mobility alternative, transit agencies must regain mode share by providing a safe and attractive customer experience. This thesis presents the design and findings of a stated preference (SP) survey conducted in Calgary, Alberta to investigate the effects of perceived COVID-19 risk, pandemic safety measures, transit service characteristics, and individual attributes on the attractiveness of transit. SP scenarios were generated using a Bayesian D-efficient design and were pivoted on respondents’ answers to previous questions. Multinomial logit, nested logit, and mixed logit models were estimated using the survey results. The estimation results show that transit agencies can attract riders by implementing mandatory masking policies and reducing in-vehicle crowding. Safety measures such as backdoor boarding and daily deep cleaning are unlikely to attract riders to transit. Higher COVID-19 risk levels, as measured by the number of daily cases in the study area, decrease the attractiveness of transit. Females and older respondents perceived transit modes as less attractive compared to males and younger respondents. Respondents who had been at least partially vaccinated perceived transit as more attractive compared to those who were unvaccinated.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".