Satisfaction in pedestrianized areas: What shapes positive and negative experiences?
Bibliographic record
Abstract
One of the approaches to improve the livability, safety, and accessibility of cities and to make neighborhoods livable places for all their residents is implementing car-free projects. Although pedestrianization provides many benefits, public resistance is sometimes directed against these projects. Based on a database of 95 variables collected through an online survey with over 1300 respondents in Montreal, Canada, this study aims to explore differences in how people experience pedestrianization and to investigate the factors shaping these perceptions. Cluster analysis is used to identify groups with varying levels of satisfaction with pedestrianization. The key differences between these groups center on attitudes toward the cohabitation of pedestrians and two-wheeler users and the opinion on the impact of pedestrianization on individuals' mobility and travel patterns. The less satisfied group with pedestrianization includes a lower percentage of females, a higher percentage of people with limitations using public and active transportation, and a higher proportion of older people. Since cyclist-pedestrian cohabitation is the variable with the highest difference between clusters, this variable is analyzed using an interpretable ensemble learning approach to better understand people's position on pedestrianization. The results suggest that having experience in cycling, a higher frequency of cycling, an agreement that pedestrians should share the car-free streets with cyclists, and a better perception of safety on car-free streets increase the satisfaction related to the cyclists-pedestrians cohabitation. • The levels of satisfaction with pedestrianization experiences are examined. • Cohabitation of pedestrians/2-wheelers impacts the level of satisfaction. • The profiles of the less satisfied group with pedestrianization are identified. • Having experience in cycling increased the cohabitation of pedestrians/2-wheelers.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".