Toward a Better Understanding of the Construction Impacts of a Light Rail System in Montréal, Canada
Bibliographic record
Abstract
Large-scale transport infrastructure projects generate long-lasting changes in the built environment and alter the lives of nearby residents. It is crucial to understand public perceptions of public-transit projects and associated construction impacts, as they influence the social acceptance and eventual success of such projects. To characterize the construction-phase experiences of a new light rail in Montréal, Canada—the Réseau express métropolitain (REM)—we analyzed data from 1,236 respondents from the Greater Montréal region who self-reported ongoing construction activities near their homes. This study employs an exploratory factor and k-means cluster analysis to group residents by their different experiences and perceptions of the REM and its associated construction impacts. The analysis returned five clusters with distinct construction experiences: construction-concerned travelers, REM-critical respondents, neutral travelers, REM enthusiasts , and rerouted travelers . Subsequently, the acceptability of the impacts during the construction phase on each cluster is assessed by comparing perceptions of the impact of neighborhood change on their quality of life and their intention to use the REM. Finally, we derive targeted policy recommendations to help promote increased social acceptability of light-rail transit (LRT) projects, including mitigating disruptions in construction zones, public information campaigns, and inclusive decision-making processes. Findings from this study can benefit policymakers and transport planners as they aim to reduce the disruptions associated with the construction of LRT systems and promote increased social acceptability.
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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 | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".