Impact of Transit-Pass Ownership on Daily Number of Trips Made by Urban Public Transit
Bibliographic record
Abstract
This paper investigates the factors influencing the decision to own a monthly transit pass and the impact ownership of the pass has on the daily number of trips individuals make by urban transit. Monthly transit pass owners in the Toronto Region are found to have a transit trip rate four times that of nonowners. A comparison of socioeconomic characteristics of transit pass owners and nonowners shows significant differences. A negative binomial model of daily transit-trip frequency is formulated. It is posited in the model structure that transit-pass ownership status is an endogenous variable in the transit-trip frequency model with the transit-pass ownership status modeled using a binary probit model. The estimation results show transit pass ownership status of an individual to be the single most important variable associated with the daily number of trips made by transit. Other variables influencing transit-trip frequency are accessibility and socioeconomic variables. Variables influencing the decision to own a transit pass include ratio of transit accessibility to auto accessibility at zone of residence, socioeconomic, and spatial variables.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".