Examining ride sourcing services as an emerging mode in Metro Vancouver: Insights into trip characteristics and impacts on multimodal competitions
Bibliographic record
Abstract
The availability and utilization of ride-sourcing services have the potential to transform how people travel. While these services could improve mobility and accessibility, they could also attract users away from active modes and public transit and increase congestion and emissions. Understanding the impacts of transportation network companies (TNCs) on the transportation system is critical to ensure that the benefits of ride-sourcing are captured, and its negative externalities are minimized. This study uses web-based survey data administered to Metro Vancouver residents to explore the characteristics of ride-sourcing trips and the early impacts of ride-sourcing use on mode choice, given that TNCs are new to the study area. Additionally, this study utilizes stated preference experiments and error-components mixed logit models to examine the influence of sociodemographic characteristics and attitudinal factors on mode choice decisions for commuting and non-commuting trips. The results offer insights into the relationship between ride-sourcing and private vehicles, local and regional transit, taxi, and active modes (such as walking and cycling). Furthermore, model results highlight the heterogeneity in mode substitution behavior across population segments. This study can help planners and agencies capitalize on the advantages of TNCs and better integrate ride-sourcing into the transportation system.
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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 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".