Barriers to car-free streets: Identifying opponents of pedestrianization in Montreal
Bibliographic record
Abstract
Urban mobility has been dominated by motorized vehicles, posing many challenges related to the environment, citizens' health and safety, and traffic congestion. Pedestrianization (converting streets to car-free zones) is a practical strategy to reduce car dependency, promote active transportation, and enhance urban livability. However, many city residents and business owners have often opposed pedestrianization. Through a cluster analysis, this study examined opposition to pedestrianization in Montreal, Canada. To this end, an online survey was designed and administered. The collected data (1909 complete responses) was synchronized with five contextual data sources to form a large-scale dataset, including 121 variables. The results suggested that opposition to pedestrianization was associated with insufficient satisfaction with 2-wheelers/pedestrian cohabitation, attractiveness, urban furniture, cleanliness, and safety of pedestrianized streets. The supporters tended to change their travel behavior to spend more time in car-free streets, while opponents tried to change their route to avoid traveling in vehicle-free zones. The opponents included more non-cyclists, males, car owners, older people, and those living alone in neighborhoods with lower density. Opponents were more likely to be drivers and taxi users. This study highlights how pedestrianization can reduce motorized vehicle use while increasing active transportation. These insights can help policymakers address public concerns and create urban spaces that better accommodate all road users. • The opponents of pedestrianization in Montreal are investigated. • A new approach is introduced to compare clustering methods. • Affinity Propagation was the best-performing model. • Opponents and supporters of pedestrianization are compared.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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".