How Has Anticipated Post-Pandemic Ride-Sourcing Use Changed During the COVID-19 Pandemic? Evidence from a Two-Cycle Survey of the Greater Toronto Area
Bibliographic record
Abstract
The COVID-19 pandemic has significantly affected activity-travel behavior in cities across the world, and in particular, travel mode choices. Studies on the topic have attributed shifts in modal preferences to the changes in attitudes toward different modes of travel that resulted from the pandemic. A common theme is that attitudes toward so-called individual modes of travel, such as private vehicles and active modes, have become more positive. In contrast, attitudes toward shared modes have become more negative. Ride-sourcing represents a relatively unique middle ground, as it combines the attributes of individual and shared modes. Given the potential for the availability of these services to influence activity-travel behavior and the operations of transportation networks before the pandemic, the potential nature of post-pandemic ride-sourcing use has important implications for transportation planning. This study uses data from a web-based, two-cycle survey to examine anticipated post-pandemic ride-sourcing usage among pre-pandemic ride-sourcing users in the Greater Toronto Area. The results highlight how anticipated post-pandemic ride-sourcing usage has changed as the pandemic has progressed, including the extent to which the determinants of anticipated usage have shifted. Notably, it was observed that changes in perceptions of risk during the pandemic influence anticipated post-pandemic ride-sourcing use. Furthermore, changes in ride-sourcing use in response to the pandemic and the utilization of ride-sourcing during the pandemic were also found to influence anticipated post-pandemic usage. Overall, the results of this study underscore the potential for post-pandemic ride-sourcing usage to differ from that of pre-pandemic usage.
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 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".