Integrating Node-Place Model With Shapley Additive Explanation for Metro Ridership Regression
Bibliographic record
Abstract
Accurate metro ridership estimation is essential for effective urban transportation planning. Traditional regression methods relying on historical passenger flow data often overlook the intrinsic relationship between network scale and land use, limiting predictive accuracy. To address this issue, this study focuses on the Beijing urban rail transit system, aiming to explore how station-level factors influence spatial variations in metro ridership over a decade (2013–2022). An advanced machine learning framework that integrates Node-Place model with Shapley Additive Explanation (SHAP-based machine learning method) is proposed. Twelve influencing factors are developed, including network centrality measures and points of interest (POI) attributes, and a CRITIC weighting method is applied to weight these variables within the Node-Place Model, ensuring an objective assessment of their relative importance. Utilizing LightGBM regression method combined with SHAP values, our model achieved an average R2of 0.88 in annual average daily entry ridership regression and a smaller discrepancy between R2and adjusted R2, significantly outperforming ordinary least squares (OLS, R2= 0.42) and geographically weighted regression (GWR, R2= 0.58). The proposed model consistently demonstrates an AIC value approximately 15% lower than alternative models across multiple years of regression tasks, highlighting its stability and superior performance. Key indicators identified include PageRank and the number of restaurants and enterprises around metro stations. Combining detailed land use data and network centrality measures with advanced machine learning techniques proves to be an effective way to enhancing ridership regression.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".