Subway Station Accessibility and Its Impacts on the Spatial and Temporal Variations of Its Outbound Ridership
Bibliographic record
Abstract
Understanding the influencing factors of subway station outbound ridership provides sights into current subway system operations and future expansion needs. The accessibility of a subway station quantifies the potential opportunities that can be accessed by its outbound riders and can be a key factor that influences its existing ridership. This study captures the impacts of 10 types of subway station accessibility on the spatial and temporal variation of the outbound ridership. The geographically and temporally weighted regression (GTWR) modeling framework was used to quantify the spatiotemporal correlation and the spatiotemporal nonstationarity among subway station outbound ridership using 1-month smart card data of one of the largest subway networks in the world (Shanghai, China) containing over 60 million exits. In addition, four separate GTWR models were estimated to capture the potential differences between regular and irregular subway riders and between weekdays and weekends. The results suggest that the GTWR model outperforms the ordinary least-square models and GWR models in both goodness of model fit and explanatory accuracy. The model estimation results highlight the spatial and temporal varying impacts of four types of subway station accessibility on the outbound ridership, including accessibility to commercial locations, bus stations, healthcare facilities, and recreation locations. The results provide valuable insights for predicting subway outbound ridership as a function of spatially and temporally explicit variables which may have implications on addressing operational, tactical, and strategic challenges related to subway systems.
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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 | 0.004 |
| 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 | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".