Get Up and Go: Evaluating Station Area Factors affecting A.M. Commuter Rail Boardings in the Greater Toronto and Hamilton Area
Bibliographic record
Abstract
This research evaluates built form and demographic factors of GO Transit commuter rail station areas that contribute to ridership and utilization of existing services in the A.M. peak period using biannual GO Rail ridership information from the Spring of 2010 to Spring of 2015. In order to identify predictors of ridership growth, four regression models were estimated that evaluated factors affecting cross-sectional ridership, cross-sectional utilization of capacity, station-level utilization at each time period, and station-level year-over-year utilization growth. Results indicate that the strongest predictors of ridership, utilization, and growth were station parking capacity and household density of the surrounding area. This suggests potential for tensions in developing GO-supportive station-area land use policy strategies focused on either expanding station-area parking capacity or station-area residential intensification. The station level utilization model produced a priority list of future service increases on GO Rail corridors based on existing capacity and ridership.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".