Exploring the Spatial Variation of Access/Egress Distances of Subway Stations Using Mobile Phone Positioning Data in Chengdu, China
Bibliographic record
Abstract
The distance from the origin or destination to or from the subway station is defined as the access or egress distance, which determines the service coverage of the subway station. However, little literature studies the distances at the station level, and they may vary from station to station. Therefore, this study aims to explore the influencing factors and spatial variation of the distances at the station level by using the mobile phone positioning data of more than 1.2 million anonymous users in Chengdu, China. First, this study proposes a method to extract the access and egress trips of the subway. Next, the ordinary least squares (OLS) regression models are carried out to select the significant explanatory variables. Finally, the geographically weighted regression (GWR) models are used to model the spatial variation relationship between the 85th percentile access/egress distances and the selected explanatory variables. The results show that different stations’ access/egress distances vary significantly in space. Hotel, residence, life, finance, road density, and mixed land use are found to be negatively correlated with distances, while education, 36–45 years old, male, and high education are positively correlated. In addition, the GWR model reveals that the influence of explanatory variables on access/egress distance varies from space to space. The results further promote the understanding of the existing system and provide a relevant reference for planners and transportation departments to optimize land use and public transportation planning.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".