Development of a Model for Evaluating the Coverage Area of Transit Center Using Smart Card Data
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Bibliographic record
Abstract
Since metropolitan cities are broadening as a result of urban sprawl, multimodal transportation systems have been adopted to fulfill the connection between the suburban and urban areas. The transportation system is being revamped around the transit center in the urban area to facilitate access to the downtown area from the suburbs. Studies are being conducted to improve the accessibility of public transportation by using the concept of hub-and-spoke. In this study, we develop a coverage area index (CAI) to assess the impact of a transit center on access to urban areas from the suburbs quantitatively. The concept of network centrality and the kernel density function is used to evaluate the extent of the influence of a transit center. The smart card data in the Seoul metropolitan area are used to analyze the CAI. Six transit centers in the Seoul metropolitan area are investigated to compare the coverage area to the transit center. The bandwidth of the kernel density function is set as 2 km considering the size and influence of each region. We evaluate six transit centers using the CAIs in Seoul compared to the index characteristics with transit accessibility (TA) index from previous studies. The CAI is possible to identify the incompetent centers, alternative routes to solve the problems of overcrowding on the centers, and areas with insufficient supplies of regional transit.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it