Analyzing the influence of transit pass ownership on the determinants of ride-sourcing frequency in Metro Vancouver: Implications for policy and urban mobility planning
Bibliographic record
Abstract
Amid the growing popularity and prevalence of ride-sourcing, there has been interest in examining the determinants of ride-sourcing frequency. Studies on the topic have noted the influence of socio-demographic attributes on ride-sourcing frequency. Additionally, limited studies have investigated the impact of public transit use and transit pass ownership on ride-sourcing frequency. However, studies of this nature treat transit pass ownership and transit use as explanatory variables. Consequently, additional work is needed to develop a more nuanced understanding of the determinants of ride-sourcing frequency. This study estimates an exogenous switching hurdle model to examine whether the determinants of ride-sourcing frequency (and their impacts) differ based on transit pass ownership among residents of Metro Vancouver. Using data from a web-based survey, ride-sourcing frequency was jointly modelled along with transit pass ownership and ride-sourcing adoption. The results highlight the potential for the determinants of ride-sourcing frequency to differ between segments of ride-sourcing users based on transit pass ownership. Overall, the results underscore the potential for the determinants of ride-sourcing use (and their impacts) to vary among different segments of ride-sourcing users. The information presented in this study can help inform efforts to mitigate the negative externalities associated with ride-sourcing by highlighting the potential value of a targeted approach to policy development.
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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 | 0.007 |
| 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) | 0.004 | 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".