Making Transit King: An Analysis of The King Street Transit Pilot
Bibliographic record
Abstract
This paper discusses the findings of quantitative and qualitative research on the initial impact of \na controversial transit pilot project which involved the redesign of a 2.6 kilometre stretch of King \nStreet in downtown Toronto. The project strictly regulated use of the area by private cars and \ninvolved a number of design initiatives to enhance the public realm for non-automobile users. \nImpacts on four stakeholder groups—transit riders, drivers, pedestrians, and business \nowners—were analyzed using quantitative data from the City of Toronto, and qualitative data \nobtained through more than 40 interviews with various stakeholders and professionals in \nToronto’s urban planning community. \nThis research finds that the initial impact of the King Street Transit Pilot has had a \npositive impact on transit riders and pedestrians. In terms of the impact on drivers and local \nbusinesses, the quantitative data shows that trends in average car travel times and consumer \nspending have remained consistent with trends established prior to the pilot being implemented. \nHowever, the qualitative data obtained from the interviews is less conclusive and revealed that \nsome businesses are reporting a decline in revenue during the pilot phase. Due to a variety of \nexternal factors identified in this paper, further research is required to determine if there is a \ncorrelation between a decline in revenue reported by some businesses and the pilot project. What \nthe data clearly shows is that overall, the King Street Transit Pilot has had a positive impact on \ntransit riders and pedestrians, and an insignificant impact on drivers and local businesses. In sum, \nthis research contributes to the well-established body of academic literature that addresses the \ncomplex mobility and congestion issues currently facing cities around the world.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".