Wheelchair Users’ Perspective on Transportation Service Hailed Through Uber and Lyft Apps
Bibliographic record
Abstract
Numerous lawsuits have been filed against Uber and Lyft for lack of disabled accessibility of the transportation service they facilitate, with some of the lawsuits focusing on wheelchair accessibility. The paper investigates accessibility from the perspective of wheelchair users and examines their perceptions, experiences, and preferences. Some of the experiences of wheelchair users have been documented in grey literature. The study investigates these in addition to dimensions that are currently unexplored: their perceptions and preferences. A survey of 341 wheelchair users in the U.S. was conducted to understand general trends and patterns. Data collected from 224 complete and 117 partial responses were analyzed using descriptive statistics and linear and logistic regressions. The findings indicate that more than 50% of respondents were satisfied with the service, but nearly 40% experienced service denial. Almost half of those without Uber or Lyft experience perceive Uber and Lyft as a viable means of transportation. The study also showed that the propensity to be an Uber or Lyft user is associated with type of wheelchair, having access to a vehicle, and level of education. The purpose of the study is to bring to the fore the lived experiences of wheelchair users by taking a larger sample than anecdotal references in media reports—where most of the current debate on this topic resides—and to gain new insights. The study fills the gap in academic literature by developing a new knowledge. It also outlines recommendations relevant for practice and policy considerations.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".