Revolutionizing TOD Planning in a Developing Country: An Objective-Weighted Framework for Measuring Nodal TOD Index
Bibliographic record
Abstract
Transit-oriented development (TOD) is a planning strategy that combines land use and transportation planning to promote economic, environmental, and social sustainability. While developed cities have embraced TOD, developing cities need to adopt it faster. This has resulted in a need for robust TOD measurement frameworks for developing countries. Furthermore, existing frameworks often use subjective weightage for different TOD indicators, which can lead to human biases. To address these issues, the authors aimed to develop a more robust and objective framework for measuring TOD in developing cities, particularly Dhaka, Bangladesh. The authors used density, diversity, destination accessibility, and design criteria to select eight indicators for measuring TOD. However, a buffer radius of 800 meters was taken for each of the 17 stations to calculate TOD. An objective-weighted spatial multi-criteria analysis (OSMCA) was used to evaluate the framework. The model’s robustness was assessed by analyzing the sensitivity of eight TOD scenarios and identifying hotspot clusters using statistical methods. Additionally, the authors ranked the stations based on the highest TOD score and compared TOD with developed and developing cities to gain planning insights. They proposed three different TOD planning methodologies for nodes that emphasize the importance of design, destination access, and density for (re)development, zoning, and affordable housing policies in Dhaka’s regions. Finally, the study discussed limitations and future research priorities.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".