Zoning In on Transit-Oriented Development
Bibliographic record
Abstract
Transit-oriented development (TOD) has been widely encouraged as a strategy to limit urban sprawl, increase urban density, and enhance neighborhood diversity. Federal and regional governments have been increasingly promoting such TOD in parallel with light rail transit (LRT) projects to foster sustainable transitions. Little is known, however, about the processes through which municipalities have made changes to existing land use regulations to achieve TOD goals. In this article we trace changes in municipal plans and bylaws surrounding a CA$7 billion LRT in Montréal (Canada) that opened in summer 2023, 7 years after its announcement. Specifically, we analyzed whether changes in municipal bylaws conformed to TOD plans recommended by the metropolitan government while exploring local barriers to zoning reform. Through policy and spatial analysis, we found that only a limited number of municipalities made sufficient bylaw changes between 2016 and 2022 to support TOD plans aimed at implementing mixed-use zoning, increasing urban density, and reducing parking ratios. Through an analysis of rezoning processes, we see an opportunity for improved multilevel cooperation, public engagement activities, and positive communication strategies in the process of building integrated transport and land use systems. These findings can aid planners and policymakers in understanding the importance of reforming municipal zoning bylaws and regional approaches to TOD, strengthening collaboration between different levels of government, and engaging in meaningful public consultation practices to foster an integrated transport and land use approach. If LRT projects are to be successful in meeting sustainability goals, greater engagement with land use regulations across multiple scales is needed to facilitate TOD.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.023 | 0.001 |
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".