Barrier-Free Transport Choices in Multimodal Cities: Understanding Perceived Accessibility of People with Mobility Disabilities
Bibliographic record
Abstract
This study examines the factors and mechanisms influencing perceived accessibility and transport choices among people with mobility disabilities (PMDs) in Quebec City, Canada. It explores physical, social, and personal factors shaping mobility in a multimodal context, including fixed-route buses, paratransit, cars, and micromobility (small vehicles designed for short, lightweight travel and usually powered by human or low-power electric motors). Participants, all members of an advocacy organization, use various mobility aids. A mixed-method approach combines survey data (35 participants) with narrative interviews (eight participants) and participant observation. Findings reveal that perceiving a transport mode as accessible does not guarantee its use. Social and personal factors, such as other passengers’ behavior, crowding, self-perceptions of autonomy, and social networks, significantly influence choices, alongside physical factors like sidewalk conditions, weather, and infrastructure. Combining usability from the interactionist Disability Creation Model (HDM-DCP) with perceived accessibility theory, we examine the overlooked framework of PMDs’ mobility decision-making in multimodal contexts, providing insights to develop more inclusive transport systems and foster barrier-free, equitable societies.
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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 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".