Bibliographic record
Abstract
Public transportation agencies are faced with the difficult task of providing adequate service during peak travel periods while maintaining adequate service for those traveling off-peak or outside a city or region's densest areas. The ability or inability of a transit system to meet these needs helps explain transit ridership rates. This research sought to understand how daily fluctuations in transit service were related to ridership in the greater Toronto and Hamilton area, in Canada, for different segments of the labor force. Many variables—including frequency and proximity of transit service, socioeconomic status, the built environment, and accessibility to employment through transit—have been linked to transit use in past research. However, many previous studies focused only on travel during peak hours. This study investigated whether fluctuations in service and demand were related to transit ridership rates. With the use of six time periods, an improved understanding of daily variation in transit mode share for commuting trips was produced. With a further division of the commuting population into two employment wage categories, it was demonstrated that the common understanding of the influences on transit ridership was potentially misleading. Commuting transit mode share and the variables that influence it are intimately related to when travel is needed and to what jobs people are traveling. To encourage transit use, agencies and researchers need to take into account commuters’ need to commute at a variety of time periods.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.006 |
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".