Among four traveller types in the Greater Toronto and Hamilton Region, who uses ride-hailing?
Bibliographic record
Abstract
Despite the rising popularity of ride-hailing, planning practitioners are still learning about the use and management of the service. This paper seeks to uncover who the primary users of ride-hailing are through a cluster analysis using traveller behaviour and mobility tool variables, where four traveller types are identified -- Multi-Modal Super-Sharers, Auto + Private Mobility Travellers, Car-Dependent Travellers, and Low Mobility Travellers. This paper finds that current auto-oriented travellers are not using ride-hailing, as demonstrated by Mobility Travellers and Car-Dependent Travellers. Additionally, ride-hailing is primarily used by non-auto-oriented travellers. The largest proportion of regular ride-hailing users, Multi-Modals Super-Sharers, are the youngest, are more educated, have access to the largest variety of mobility tools, and travel the most. For Low Mobility Travellers, the most vulnerable group based on household income, educational attainment, employment status, and car ownership, ride-hailing is filling a transportation gap. Understanding who uses ride-hailing is a key component in understanding the potential changes in travel behaviour.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".