Pedestrians’ and Cyclists’ Preferences for Street Greenscape Designs
Bibliographic record
Abstract
In recent years, the public’s interaction with street green spaces has been increasing, leading to much more concern about its design. By using stated preference data from a discrete choice experiment and the multinomial logit model, this study investigates pedestrians’ and cy-clists’ landscape preference regarding street green space through an online survey based on a virtual street envi-ronment. The results show that trees are the most suitable to be planted symmetrically between the cycle track and sidewalk. Large size trees with large crown width and tall height are more preferred than common size trees. There are considerable differences in preferences for lo-cations of shrubs, hedges, flowers, and grass between cy-clists and pedestrians. Cyclists prefer grass by the cycle track the most and grass by the sidewalk the least. But for pedestrians, flowers, hedges, and grass by the sidewalk are positively significant. Buildings with green plants in their front yards are preferred over a monotonous facade or coffee seats. This study enriches the understanding of the public’s landscape preferences for streets sharing non-motorised lanes. The results also play a guiding role in people-oriented street green space designs of land-scape architects and governments.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".