Human‐centered designs, characteristics of urban streets, and pedestrian perceptions
Bibliographic record
Abstract
Summary This paper presents the results of a study conducted to examine the characteristics of human‐centered design and pedestrians' perceptions of street design features. The main emphasis was to determine the existence of empirical evidence that human‐centered design increases pedestrian satisfaction levels and enhances community walkability. The following approach was applied in the study: (i) the existing research concerning walkable community and pedestrian facility designs was reviewed; (ii) survey data from pedestrian interviews regarding urban streets as well as the detailed geometric features of the interview sites were gathered; (iii) statistical analysis to determine whether pedestrians actually feel more satisfied when they walk in areas with human‐centered design was conducted based on actual pedestrian interview scores for various street design features; and (iv) major design features to increase pedestrian satisfaction levels were identified. The study results show that pedestrians perceived planting strips as the most important design element that would increase the satisfaction scores whereas they perceived the presence of driveways and the number of vehicle lanes as design elements that that would diminish the scores. Overall, the valuable findings of this research provide evidence of the various effects of the application of human‐centered design and improve our understanding of walkable communities. Copyright © 2015 John Wiley & Sons, Ltd.
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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 | 0.000 | 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 | 0.000 | 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".