Use of Rear-Facing Position for Common Wheelchairs on Transit Buses
Bibliographic record
Abstract
This synthesis will be of interest to transit agency staff and those who work with them in dealing with wheelchair securement on transit buses. It offers information on existing programs in many countries and documents transit agency experiences for the benefit of others considering similar deployments, in particular with respect to the Americans with Disability Act (ADA) and to its use in U.S. Bus Rapid Transit (BRT) systems. The report describes the state of the practice with respect to the use of rear-facing position for accommodating common wheelchairs (as defined by the ADA) on large transit buses (more than 30,000 lb) and identifies pertinent issues related to its transferability to the U.S. context. This report integrates the information obtained from a literature review, gathered from many sources and countries. Agency surveys of all Canadian transit systems that have adopted the rear-facing position, case studies, and interviews with key experts in several other countries (the United Kingdom, France, Germany, and Sweden, as well as communications with Australian experts) were conducted to obtain information and to offer better insights. Case studies were conducted at British Columbia Rapid Transit (BC Transit), Victoria, BC, Canada, and Mississauga Transit, Mississauga, Ontario, Canada. Additionally, extensive discussions were held with Alameda-Contra Costa Transit (AC Transit) staff, Oakland, California--the first U.S. transit agency to design a rear-facing position in their 2002 order of transit buses to be used in a planned BRT deployment.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".