The Evolution of Worldwide Metro Systems: A Study on Their Scales and Network Indexes
Bibliographic record
Abstract
Based on the statistics of 121 metro systems in the world, this paper analyses the worldwide metro construction from 1863 to 2003. Linear regression is introduced to show the relationship between the metro scale and urban population, as well as surface area. And by applying the hierarchical cluster analysis, three types of metro systems are identified in terms of their scales and network indexes: the first metro system with 10 samples, which is huge and with complex network, such as London and New York; the second metro system with four samples, which is relatively big but with limited network patulous potential, such as Athens and Vienna; and the third metro system with 107 samples, which is relatively small but has great patulous potential in network, such as Beijing and Montreal. Our findings suggest that metro scale, as well as network indexes, show great differences among five major continents. Metro systems in Europe and North America embrace a more mature network; however, metro systems in Asia and South America have greater network patulous potential. Also we find that in most cases urban population has more impacts on metro scale, especially to the samples in the third metro system. However, when it comes to the first metro system, surface area has significant impacts on metro scale. Finally three linear regression models are implemented to calculate the theoretical scales of 53 Chinese major cities, and it shows that theoretic metro scales in these cities range from 37.2 km to 129.9 km. These metro systems can be divided into four classes based on their model calculations. According to the result, Beijing, Shanghai and Chongqing should develop the biggest metro systems in China. With the progress of urbanization in China, metro systems will grow rapidly in the coming years, especially in the developed regions.
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How this classification was reachedexpand
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 | 0.000 | 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 | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".