The "ugly" TTC subway map (and how it ruins your mood and movement in the city) : a case study and redesign
Bibliographic record
Abstract
The TTC subway was built in 1949 to resolve the traffic issues of that era, issues that have only since intensified. At the time, any underground transportation system was an impressive accomplishment in design, engineering, construction, and city planning. Today, those same accomplishments—left to stagnate, age, and become overburdened—have become outdated and—measured against contemporary designs—sometimes even ridiculed. As the TTC continues trying to expand its infrastructure to meet the demands of a growing urban population, its progress leaves much to be desired—past decisions made without the foresight of urbanization, globalization, and technological innovation are being revealed to be inadequate. What we are left with is a face lift and hair extensions for a transit system that actually needs a brain transplant and genetic modification. But while this Major Research Project acknowledges the infrastructural inadequacies of Toronto’s TTC metro system, the focus here will specifically be on the TTC’s transit maps, branding, and graphic design which itself, I will argue, has not aged gracefully and is in serious need of an update—one that responds to and satisfies the needs of today’s mobile, increasingly design-savvy, and digitally connected citizens. Keywords: subway, map, representation, branding, wayfinding
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.012 | 0.009 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".