The effects of urban form on public transportation demand in a developing city
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.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".