Finding the Subway Disruption Regimes of Switching Subway to Uber in Toronto
Bibliographic record
Abstract
The evolving relationship between public transit and transportation network companies (TNCs), such as Uber and Lyft, is of great interest to government agencies and has seen much critical attention in the academic literature. In this paper, we focus on the demand for TNC trips (also known as ride-hailing trips) during the disruption of the subway service. We combined a detailed dataset of Uber trips made in Toronto, Canada during the period September 2016 to August 2018 and subway disruption data provided by the Toronto Transit Commission. These data were used to examine the question: how long are subway users willing to wait during a disruption before switching modes? This question was framed as a threshold point, and an innovative structural threshold regression model was used to obtain an answer. Controlling for environmental and location-specific factors in the model, it was revealed that subway users in Toronto tend to switch to Uber after a service delay of as little as 3 min, with an average result of 7 min and an upper bound of 12 min.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".