Direct Ridership Model of Rail Rapid Transit Systems in Canada
Bibliographic record
Abstract
A direct ridership model for Canadian rail rapid transit systems is presented. The goal of the study was to produce a ridership model to evaluate the specific context of Canadian rapid transit: no comprehensive model existed. Data were collected for Canada's five largest cities, including 342 stations with an average weekday ridership of more than 3 million passengers. Using bootstrapped ordinary least squares regression with station boardings as the dependent variable and 44 socio economic, built environment, and system attributes as potential explanatory variables, which were chosen after a review of the direct ridership model literature, the study yielded one model with an adjusted R 2 value of .8033. The results are similar to those of models constructed in the United States with respect to densities, land uses, and station amenities, and socioeconomic variables do not appear to be significant. The absence of socioeconomic variables in the final model indicates that planners and policy makers have significant scope to exert influence over transit use through land use planning, design, and service features.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".