Improving urban accessibility: A methodology for urban dynamics analysis in smart, sustainable and inclusive cities
Bibliographic record
Abstract
Despite the improvisations of current urban accessibility regulations and their application in urban systems, it is a fact that our cities are not accessible. Both, the assessment of the effectiveness of urban accessibility and its maintenance over time are issues that require a more consistent approach. In order to address these aspects, it is necessary to have an accurate awareness of the existing condition of urban accessibility. Therefore, the way this information is transformed into specific data, which must be collected, stored and assessed, is one of the main challenges that smart cities face. This research helps implement an integrated system for urban accessibility analysis, combining the latest advances in the Information and Communication Technologies, such as RF & GPS positioning, smart sensing and cloud computing. The main goal of this research is to develop a reliable and effective method to assess public space accessibility with special focus on people with disabilities, by eliciting from users personal experiences. Consequently, the data obtained will enable a better design for improving pedestrian mobility. As a result, a computational architecture for urban dynamics analysis has been designed. Finally, technology and data processing have been validated as an effective system for data collection, and, as a first approach to users' real experience, it has been proposed to have a testing scenario at the University of Alicante,.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".