Data Driven Transit Oriented Development Planning: Using Montreal’s New Transit System as a Case Study
Bibliographic record
Abstract
The goal of transit-oriented development (TOD) is to encourage personal movement via walking or cycling to/from transit stations, increase the usage of shared means of transit, reduce highway, street, and parking congestion, and thereby instill both personal and environmental benefits. There has been a significant amount of research on the methods regional and city planners can use to identify and pursue opportunities for TOD. The primary focus of most prior research has centered on population density, walkability, land use diversity, and parking around potential transit nodes to identify which ones are best suited for TOD. Studies frequently aggregate these factors into a single TOD index. However, several key considerations have been omitted in past research and applications related to TOD, such as real estate development capacity, and market potential. This thesis aims to assess TOD potential quantitatively from both a city planner and real estate developers’ perspective for an existing transit network and new networks in the planning phase. As a result, city planners and real estate developers could co-create maps and indices to identify which existing stations would best serve as a new polycentric node, and where new transit lines should be placed to maximize the benefits associated with TOD. To address this gap in the prior literature and its practical applicability, this research proposes a unique methodological approach that focuses on development potential around major transit nodes and market potential. This new proposed methodology produces a spatial index with three distinct layers: the Walkability, the Potential Densification, and the Real Estate Market. Along with presenting a methodological approach, the methodology will be applied in a case study, using use the city of Montreal as an illustrative example.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".