Mind for mass transit: Commuters’ assessment of public transport as a “reasonable” option
Bibliographic record
Abstract
Retaining and increasing public transport ridership is a centerpiece of many strategies to address both the climate crisis and public health challenges.Understanding how and why commuters choose or reject public transport as a viable option or actual mode is, thus, central to policymakers' efforts.This study makes use of a detailed travel-behavior survey conducted at McGill University in Montreal, Quebec, to answer two key questions: (1) What factors influence travelers' perception of public transport as a reasonable commuting option?and (2) From among those travelers that do consider public transport to be reasonable, what factors influence their final decision to use it.One important finding is that there is sometimes a disconnect between the factors that influence a person's initial assessment of reasonableness and subsequent mode choice.For example, car owners were paradoxically more likely to consider public transport a reasonable option but significantly less likely to use it.More generally, another important finding of this study is that there may be a sizeable contingent of travelers who consider public transport to be a reasonable or viable option but nonetheless decline to use it.It may prove easier to convert these travelers to public transport, making it important for policymakers to understand their motivations.Ultimately, public transport agencies may be able to use this type of information to develop policies better targeted as bolstering ridership.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 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".