Evaluating Transit‐Oriented Development Performance: An Integrated Approach Using Multisource Big Data and Interpretable Machine Learning
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Transit‐oriented development (TOD) strategies on subway stations have been implemented in many high‐density cities globally to enhance public transportation system efficiency and promote public transportation mobility. Focusing on the developments of intricate metropolitan systems, researchers attempted to elicit “latent rules” by proposing a generic TOD performance evaluation system. This study suggests a multi‐indicator TOD performance evaluation method based on a multi‐indicator approach grounded in the analysis of multisource urban big data, revealing the role of rail transit TOD station characteristics on critical indicators of station operation through an interpretable machine learning approach. Using Shanghai, China, as a case study, the methodology employed 26 widely used indicators related to TOD development and utilized a BP neural network model trained in a sample space of 77 rail transit TOD stations, aiming to predict the four critical station performance indicators. The robustness of the explanatory variables in the model has been verified by various methods, affirming their consistencies with the development characteristics of the city and the stations. The performance assessment methodology achieves significant predictive results and is computationally feasible, with potential values in applications in other high‐density cities worldwide.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it