The effects of urban form on public transportation demand in a developing city
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
Rapid urban growth in developing cities alters urban form, which directly and indirectly impacts access to public transit. Therefore, to accurately predict future public transit usage in order to achieve a sustainable public transportation system, it is essential to understand how each urban form indicator influences demand. However, most previous research has focused primarily on the Global North or China. Therefore, this study aims to fill that gap by analyzing the effects of urban elements on public transportation demand in a developing city. To do so, after a comprehensive review of relevant studies, effective elements of urban form were identified. Then, using spatial statistical analysis, a database of the urban form and travel characteristics was assembled, and random forest regression was employed to examine the relationship of different urban form indicators with public transit usage. The model achieved a good fit and, using a game-theoretic interpretability technique revealed that most variables had consistent associations with the findings from studies in other parts of the world. However, a few variables exhibited different associations, such as distance to educational land use. Additionally, some variables had opposite associations depending on whether they were at the origin or destination of the trip, such as distance from the city center. Therefore, it is concluded that the impact of each factor on public transportation demand should be evaluated on a case-by-case and an origin–destination basis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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