Enhancing sidewalk accessibility assessment for wheelchair users: An adaptive weighting fuzzy-based approach
Bibliographic record
Abstract
To reach a destination within the community, it is crucial that wheelchair users possess the ability to plan, execute, and acquire knowledge of routes in a safe and efficient manner. While numerous methods have been introduced for assessing the accessibility of sidewalks, existing studies often overlook the variations in the perception of the accessibility of long segments based on each wheelchair user's capabilities. Extended distances may lead to increased fatigue, impacting the ability of individuals with mobility disabilities to navigate sidewalks comfortably and independently. In this paper, we propose an adaptive weighting method, effectively addressing the accessibility assessment of sidewalks by considering more specifically the impact of sidewalk length. The results underscore the significant impact of sidewalk length on mobility, delineating varying accessibility indices in long sidewalk segments, and offering a more realistic evaluation of accessibility based on wheelchair users' perceptions. For validation purposes, the proposed model was implemented in a personalized routing tool called MobiliSIG and compared with the conventional fuzzy model provided by the tool for accessibility assessment through a case study in Quebec City. The results demonstrated improved routing outcomes compared to previous methods, showcasing the effectiveness of our model in enhancing sidewalk accessibility assessment.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".