Summary The BART system, built in the San Francisco Bay Area in the 1960s, was the first regional rail
Bibliographic record
Abstract
system to be built in the U.S. in more than 50 years. Since then, urban rail systems have been completed in ten cities on the West Coast and in Vancouver, Canada. These cities have had varying levels of success in attracting transit-oriented development (TOD). Seattle can learn from these experiences, so it does not repeat mistakes others made and takes advantage of opportunities presented. To understand more about what tools work best, this paper presents detailed case studies of representative transit-oriented development projects throughout North America. Lessons from these case studies and the implications for Seattle are discussed. These lessons will help evaluate what actions makes most sense for the city and its neighborhoods. The twelve cases of transit-oriented development were selected because they represent comparable light rail station types and/or physical settings or because certain types of implementation tools were used to make transit-oriented development happen. In looking for comparable examples of transit-oriented development in North American cities, specific station area characteristics were evaluated: whether the station is underground, at-grade or elevated, how many people use the station, surrounding urban form and land use, and what other transportation connections is provided. The cases selected provide valuable insights that will help the City ensure that station area plans meet the City’s goals and avoid the mistakes that have limited transit-oriented development elsewhere. CASE STUDIES This analysis of TOD case studies looks at a variety of transit operators, cities, and station types throughout North America. Although the case studies make reference to many exemplary station-area projects within the transit corridors served, the following stations are reviewed in the most detail:
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 0.002 |
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".