A methodological framework to generate transit-oriented development (TOD) typologies
Bibliographic record
Abstract
To support the implementation of transit-oriented development (TOD) in different contexts, researchers and planners increasingly use TOD typologies. While the methodologies developed to generate TOD typologies are frequently meant to be transferable across different contexts, no studies explicitly analyze and describe how to adapt these methodologies to other regions. Therefore, this paper examines which factors need to be considered when applying a TOD typology methodology to any given region. This paper provides a detailed description of the process of adapting an existing bicycle-oriented TOD typology methodology that was developed for Montreal (Canada) to another context in Rotterdam (the Netherlands). The transfer results in two distinct typologies by clustering stations based on their respective contexts. Based on the findings, we designed a five-step methodological framework to guide TOD typology development across diverse contexts. The five steps are: (i) identify the study area (public transport stations) and define the research or planning objectives underlying the development of the typology, (ii) identify indicators and the catchment area, (iii) select the classification methods, (iv) present the clustering results, and (v) discuss planning implications. This study is not a comparative analysis of classification results between Montreal and Rotterdam, but rather a critical analysis of the transferability of a TOD typology methodology, which has implications for any planning tool that is used beyond a single context. The proposed framework makes developing a TOD typology more accessible to planners and, therefore, supports the use of TOD typology in practice. • Transfer of a TOD typology tool from Montreal, Canada to Rotterdam, Netherlands. • Five-step methodological framework to design context-specific TOD typologies. • Contextualization of the tool based on the study area and planning objectives is key. • Proposed framework enables local station development policy recommendations.
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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 | 0.070 | 0.074 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.018 | 0.014 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".