Automated generation of concentric circles metro maps using mixed-integer optimization
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The concentric circles (CC) map design is an alternative approach for schematically representing metro systems. Compared with traditional octo-linear maps, CC maps can effectively simplify the perception of a network by visually accenting circular line patterns. This design offers new insights into the schematic drawing of metro systems that can improve map readability and engagement. Automated mapping studies in the literature have mostly applied the traditional octo-linear design using optimization methods, where design criteria are modeled as constraints and/or objective functions in a constrained mixed-integer optimization program, whereas the automated CC map drawing approach has received less attention. In this article, we develop an automatic CC map drawing method by adopting map design criteria as a mixed-integer programming problem. Numerical experiments are conducted using (a) a simple network to illustrate the model procedure in detail, (b) two real-world metro networks in Vienna and Montréal to analyze the effects of the selected map center and parameter settings and (c) the Beijing subway to analyze the applicability of the proposed approach to large-scale metro networks.
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.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 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