Bibliographic record
Abstract
Rail rapid transit forms the backbone of many public transportation systems in cities globally moving people at both high speed and at high capacity. As cities seek to alleviate problems of congestion and environmental pollution many are constructing or expanding urban and suburban rail networks including in Canada where in 2015 numerous projects were underway or recently completed. Traditionally travel choices have been considered to be products of time and monetary cost academics and researchers have resented strong evidence also linking travel behaviour to factors including the built environment, station amenities, and street networks. \nThis thesis links local station level factors, including built form, street network, station amenities and service, and socioeconomic characteristics, and rail rapid transit ridership in Canada. A direct ridership model (DRM) approach is used with OLS, robust, and two-stage least squares regression and bootstrapping is used to enhance the models. Data was collected for from 342 station locations in Canada’s five largest metropolitan areas with an average weekday ridership of over 3 million. Average weekday station boardings were used as the dependent variable and 53 socioeconomic, built environment, and system attributes were chosen as potential explanatory variables that were chosen after a review of the DRM and travel demand literature. The study yielded three sets of models with an adjusted r-squared values ranging between 0.650 and 0.864. Canadian rail rapid transit stations were tested together and separately as urban and suburban service types. The most important factor identified in the models was the supply of transit service, followed by inter-modal connections (bus stops for urban stations and primarily parking for suburban stations), and residential population density. Socioeconomic factors of the population in the area surrounding stations were not found 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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".