Investigating Changes in Ride-Sourcing Use during the COVID-19 Pandemic: Evidence from a Two-Cycle Survey of the Greater Toronto Area
Bibliographic record
Abstract
The rapid spread of the SARS-CoV-2 virus has resulted in changes in modal preferences, leading to an increased preference for individual modes (e.g., private vehicles and active modes) and a reduced preference for shared modes. However, ride-sourcing represents somewhat of a middle ground between individual and shared modes, given the relatively limited number of interactions with strangers. Consequently, these services have the potential to serve as an alternative to public transit, particularly for those without a private vehicle. Given the extent to which ride-sourcing impacted transportation systems prior to the pandemic, as well as the impacts of the COVID-19 pandemic on modal preferences, it is essential to understand the short- and long-term impacts of the pandemic on ride-sourcing use. The goal of this paper is to examine how ride-sourcing use, attitudes toward ride-sourcing services, and the anticipated use of ride-sourcing in the postpandemic period have changed over the course of the COVID-19 pandemic. The data for this study were obtained through a two-cycle survey conducted using a web-based interface in the Greater Toronto Area. The results suggest that ride-sourcing use and attitudes toward ride-sourcing services have rebounded from the initial impacts of the pandemic and that these services could be acting as an alternative to public transit. Additionally, the results highlight how changes in the utilization of ride-sourcing over the course of the pandemic can vary based on factors such as age, household income, and household vehicle ownership. The findings presented in this study can be used to help identify trends in ride-sourcing use that should be monitored both during and after the pandemic. This information can assist in the development of future data collection programs that can inform policies that aim to address the negative externalities of ride-sourcing services.
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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 | 0.004 |
| 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".