Joint Model of Transit Usage Frequency and In-Vehicle Safety Perception During the COVID-19 Pandemic
Bibliographic record
Abstract
Social distancing strategies and strict hygiene adherence during the pandemic have added an extra dimension to the safety requirements of transit usage. Thus, travelers’ altered safety perceptions, which can affect transit usage, need to be assessed for effective policy decisions for the post-pandemic period. This study examined the interaction between in-vehicle safety perception and transit usage using an integrated approach by jointly modeling them, considering the fear of virus infection. A multivariate ordered probit model was developed for the investigation using a dataset collected through a web-based travel survey conducted in the Greater Toronto Area, Canada. The results reveal that, along with socioeconomic attributes, many pandemic-related variables and latent attitudinal factors affect the propensity to use transit. It is observed that those having a better safety perception of the bus are more inclined to use transit more frequently than others. Apart from safety perception, those who were more cautious, over the age of 34, and shifted to working from home during the pandemic had an adverse propensity to use transit. However, a higher propensity toward transit usage was observed for pre-pandemic transit users and for those who had a higher level of satisfaction with transit attributes during the pandemic. A similar tendency was also observed for fully vaccinated residents.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".