The Application of Data Envelopment Analysis for Transportation Planning based on the Viewpoint of Economic Efficiency
Bibliographic record
Abstract
There has been an ever increasing interest in the urban planning techniques, stimulated by the possibility that design strategies associated with the built environment be used well to control, manage, and shape individual behavior and socio-economic activity in cities. The numerous available evidence suggests that a combination of urban design strategies and Transit-Oriented Development (TOD) will help encourage our communities more livable, sustainable, and promoting the quality of our urban life. Therefore, the development of appropriate design techniques of TOD has become increasingly important as the TOD planning mode applied in the urban built environment. This study will try to integrate and classify the category of smart growth principles based on literature review. Followed by Fuzzy Delphi Technique (FDT) for obtaining expert’s opinions and screen the most important criteria of guiding principles. And then the empirical study of Taipei Metro Transit System will be demonstrated to show the application of our proposed methodology as well as framework. Finally, the adoption of Fuzzy Analytic Hierarchy Process (FAHP) method and Data Envelopment Analysis (DEA) model which combined with assurance region analysis will be applied to measure and select the most suitable MRT stations of Taipei Metro Transit System.
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".