Relationship between built environment and metro ridership: machine learning analysis
Bibliographic record
Abstract
This study examines the driving factors and nonlinear effects of built environment (BE) on passenger flow within the influence areas of rail transit stations. A method is proposed to classify stations and characterize their passenger flow patterns, considering spatial connectivity and transit-oriented development-BE development levels. The Extreme Gradient Boosting (XGBoost) model coupled with the Shapley Additive Explanations value algorithm was employed to identify the key determinants of passenger flow and to explore their nonlinear relationships. Results demonstrate that the XGBoost model outperformed the Multiscale Geographically Weighted Regression (MGWR) model, exhibiting minimal prediction error fluctuations and achieving the highest R 2 value of 0.85. Indicators related to station fineness and service capacity were found to exert the most significant influence on passenger flow. Furthermore, the nonlinear relationship between the BE factors and passenger flow reveals specific thresholds, offering guidance for station spatial layout and the optimization of operational strategies according to station typologies.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".