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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".