A prioritization framework to identify key attributes of transit-oriented development (TOD) using multi-criteria decision-making (MCDM) approach: an Indian context
Bibliographic record
Abstract
Transit-oriented development (TOD) has emerged as a crucial urban and transportation planning tool, addressing major urban challenges in India’s metropolitan regions. This study utilized six multi-criteria decision-making (MCDM) approaches to rank 16 TOD attributes selected through literature review and expert consultation. Input from 35 experts, including planners, researchers and policymakers, was gathered using a Likert scale questionnaire to determine the most significant attributes in the Indian context. The findings highlight transportation affordability, first and last-mile connectivity and multimodal integration as the top-ranked TOD attributes, emphasizing their importance for sustainable and inclusive urban development. Conversely, street-oriented developments, informal sector integration and housing diversity ranked lowest, reflecting their reduced perceived significance in this context. These rankings provide valuable, context-sensitive insights for prioritizing resource allocation and formulating effective TOD strategies in developing nations. By focusing on key infrastructural elements that enhance accessibility and sustainability, policymakers and planners can create more efficient and appealing TOD neighbourhoods. These findings serve as a guide for resource allocation and decision-making, enabling the development of vibrant, well-connected urban communities that address the needs of a rapidly urbanizing population in India and similar developing countries.
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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 | 0.012 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.013 | 0.009 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".