Bibliographic record
Abstract
The most common form of access to urban transit is by foot. Early suburban and exurban commuting to urban centers was facilitated first by commuter rail along existing intercity rail lines and then by interurban services that were often electrified. Since these services generally connected town centers most access to them was by foot and occasionally by horse. The rise of automobility in the early decades of the twentieth century facilitated by increased roadbuilding and paving, led to greater automobile commuting or driving to stations. The provision of park and ride facilities was greatly influenced by shifts in U.S. transportation policy and funding, beginning in the 1960s. The park and ride idea has been imported by many transit systems around the world, although most have made significant departures from the American model in both form and provision. Walk-to and drive-to transit are compared and the consequences of investment in park and ride and “kiss and ride,” especially in the U.S. and Canadian contexts, are explored. It is found that drive-to transit comes with considerable environmental, fiscal and opportunity costs and that its funding could be applied more productively to improving local transit and pedestrians conditions.
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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.111 | 0.035 |
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".