Transit Pass Ownership as a Potential Source of Heterogeneity in the Determinants of Ride-sourcing Use in Metro Vancouver
Bibliographic record
Abstract
Abstract Amid the increased utilization of ride-sourcing, the relationship between these services and public transit received significant attention from policymakers and researchers. Prior studies have found that ride-sourcing has mixed impacts on transit ridership and that transit use tends to be positively associated with ride-sourcing use. However, these studies have primarily treated transit use and transit pass ownership as explanatory variables. Given the potential for ride-sourcing to influence transit use, further work is needed to understand the relationship between these services. To explore whether transit pass ownership is a source of heterogeneity among ride-sourcing users, this study uses an econometric model to explore the impacts of transit pass ownership on the determinants of ride-sourcing use in Metro Vancouver. Using data from a web-based survey, a two-stage model is used to jointly model transit pass ownership, ride-sourcing adoption, and ride-sourcing frequency. The results demonstrate the potential for transit pass ownership to be a source of heterogeneity among ride-sourcing users. Specifically, the factors influencing ride-sourcing use (and the elasticity of these factors) were found to differ based on transit pass ownership. Additionally, the results suggest that attitudes and perceptions toward ride-sourcing can influence transit pass ownership. These findings can help inform targeted approaches to limiting the negative impacts of ride-sourcing on transit use.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".