Integrated Multimodel Evaluation of Transit Bus Emissions in Toronto, Canada
Bibliographic record
Abstract
This paper investigates transit bus emissions in the city of Toronto, Ontario, Canada, by linking the results of a microsimulation transit assignment model, MILATRAS (microsimulation learning-based approach to transit assignment), with emission factors derived from Mobile6.2C. Emissions were estimated at the level of individual buses during idling conditions at bus stops and on roadway links between stops during the morning peak period. The busiest routes were associated with the highest total emissions as a result of a combination of high ridership and lower speeds; this association confirmed the common wisdom that newer, low-emitting buses should be first allocated to these routes. The highest dwell emissions occurred at intermodal transfer stations (bus to subway and vice versa). On a passenger kilometer basis, the highest-emitting routes were not the busiest, but rather were those with the lowest ridership. In fact, the highest emissions per passenger kilometer were associated with the Airport Rocket, a route that provided service to the airport and was characterized by low ridership in the morning peak period. On average, bus trips in Toronto were about three times more fuel efficient than were private car trips and created 20 times less carbon monoxide pollution. The effects of changing fuel types and fleet age on transit bus emissions were assessed. Implications for bus operations are discussed relative to fleet allocation to minimize total emissions.
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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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".