Exploring the relationship between the urban built environment and elderly pedestrian mobility in South Asian cities
Bibliographic record
Abstract
Abstract Cities in South Asia have traditionally been dominated by pedestrians for their daily trips. As the elderly population is increasing in the last two decades, the dynamics of designing appropriate walkways to serve elderly people are getting more attention from urban planning scholars and policymakers alike. However, few studies in planning in the context of southern cities have considered the significant issue of elderly mobility and walkability in cities in South Asia beyond the realm of large metropolitan cities. In this paper, we attempt to understand the challenges encountered by elderly pedestrians in existing street conditions and summarizes information that may be useful for enhancing elderly mobility. Using cases of Rourkela in India and Khulna city in Bangladesh, we have collected both primary and secondary information by conducting a structured questionnaire survey in both cities at a similar period. Further to this, we analyzed statistical models to understand relationships among built environment and mobility issues based on subjective evaluation (i.e., infrastructure, street design, lighting, overcrowding condition, and encroachment). Most of the elderly pedestrians surveyed in both cities demand improvement of micro-scale urban design features and planning guidelines that they assume are absent in the statutory planning documents. This study may be employed as a useful document for city-level planning taking into account elderly perception about the built environment and their mobility concerns in future policy and planning projects. Consequently, a more comprehensive study may be incorporated highlighting elderly pedestrian’s mobility within the formal/informal transportation planning system.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".