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.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".