Network Analysis of World Subway Systems Using Updated Graph Theory
Bibliographic record
Abstract
This paper demonstrates that network topologies play a key role in attracting people to use public transit; ridership is not solely determined by cultural characteristics (North American versus European versus Asian) or city design (transit oriented versus automobile oriented). The analysis considers 19 subway systems worldwide: those in Toronto, Ontario, Canada; Montreal, Quebec, Canada; Chicago, Illinois; New York City; Washington, D.C.; San Francisco, California; Mexico City, Mexico; London; Paris; Lyon, France; Madrid, Spain; Berlin; Athens, Greece; Stockholm, Sweden; Moscow; Tokyo; Osaka, Japan; Seoul, South Korea; and Singapore. The relationship between ridership and network design was studied by using updated graph theory concepts. Ridership was computed as the annual number of boardings per capita. Network design was measured according to three major indicators. The first is a measure of transit coverage and is based on the total number of stations and land area. The second relates to the maximum number of transfers necessary to go from one station to another and is called directness. The third attempts to get an overall view of transfer possibilities to travel in the network to appreciate a sense of mobility; it is termed connectivity. Multiple-regression analysis showed a strong relationship between these three indicators and ridership, achieving a goodness of fit (adjusted R 2 value) of .725. The importance of network design is significant and should be considered in future public transportation projects.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".