Planning Urban Ring Rail Transit Lines: Case Study of Shanghai, China
Bibliographic record
Abstract
Shanghai, China, has the largest metro system in the world, with a network length of more than 550 km. Both Shanghai and Beijing are among the top five cities in terms of ridership, and some of the most important components of their metro systems are the ring transit lines. Many other cities, in China and elsewhere, also envision a ring transit line for their future rail transit networks. A previously developed analytical model for the long-range planning of ring transit lines was used in the comparison of the current alignment of the Shanghai ring line with the optimized model output, and a second ring transit line was recommended for the future Shanghai network. The findings suggest that the alignment of an existing ring line would affect the optimal alignment of the second ring line. In addition, if an outer ring line exists (or is planned to be constructed), the optimal location of the inner ring line might not be its current location. Furthermore, a sensitivity analysis was conducted to test the impact of changes on demand, value of time, and passenger ride cost on the second ring line. Zones that would benefit most from introduction of the second ring line were also determined. Although the case study presents the Shanghai ring lines, the outcomes provide useful information for other cities that are considering the expansion of their transit network with a first or second ring line. Unlike simulations and agent-based models, the model presented in this study is easily transferable to any transit network.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".