Who uses ride‐hailing? Policy implications and evidence from the Greater Toronto and Hamilton Area
Bibliographic record
Abstract
While many are eager to guide policy decisions on ride‐hailing, understanding the broader social and travel implications hinges on local contexts. Towards providing policy guidance in the Canadian context, this paper explores how mobility sub‐markets are related to ride‐hailing use in the Greater Toronto and Hamilton Area. Using data from a 2018 travel survey, cluster analysis is used to identify four traveller sub‐markets which are compared with ride‐hailing use. The first comprises “multi‐modalists,” who are younger, mobile, educated, and represent the largest group of ride‐hailing users. Second are users in two auto‐oriented markets—“private modalists” and “auto dependents”—who are older and least likely to engage in ride‐hailing. Finally, “low‐mobility travellers” are the most socio‐economically vulnerable and have lower household incomes, lower education attainment, lower likelihoods of being employed, fewer cars per household, and the highest reliance on public transportation among users in the traveller sub‐markets. Nevertheless, this group represents the second largest market for ride‐hailing, which fills a mobility gap in the absence of auto access. Understanding the social and travel behaviour implications from ride‐hailing is important towards crafting context‐appropriate public policy. Results from this study suggest that ride‐hailing is primarily used by those who are already highly mobile (multi‐modalists) and those who are most vulnerable (low‐mobility travellers).
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".