Assessing the Effect of the King Street Transit Pilot on Residential Traffic Noise Exposure
Bibliographic record
Abstract
The noise in urban environments continues to grow as more of the world's population transitions to cities. Vehicular noise is one of the main factors of noise pollution in cities. Toronto recently introduced a Transit Priority Corridor which reduced the total traffic flow along one major road. High levels of noise can have serious health impacts on city residents who work and live in dense urban cores. This study aims to model the noise on the façades of buildings in the City of Toronto. Two models were created using traffic flow data modelled from measured vehicle counts. SoundPlan a noise modelling software, was used to create façade level noise maps and to estimate the residential exposure. In the study area surrounding King Street, over 90% of the population in the study area was found to be exposed to levels described by the World Health Organization as dangerous. In the study area, 91.66% of the population based on the modelled results was exposed to levels above 45 db(A) before the pilot and 92.45% were exposed after the pilot. Noise disturbance at this level is associated with many long-term health impacts that can be mitigated by reducing source noise. Transit efficiency management systems like the King Street Pilot program can be used to reduce source noise although programs along only one stretch or road are unlikely to significantly reduce noise levels
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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 | 0.000 | 0.002 |
| 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) | 0.002 | 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".