Harnessing the Potential of Automated Data to Simulate Emissions of an Interregional Bus Route in Toronto, Canada
Bibliographic record
Abstract
This study made use of automated vehicle location and automated passenger counter data to simulate the greenhouse gas (GHG) emissions of an interregional bus route in Toronto, Ontario, Canada. The authors analyzed bus performance and emissions as well as quantified emissions under the effects of operational improvements (increasing speed and reducing idling) and different fuels (conventional diesel, compressed natural gas, and biodiesel). Average total trip emissions were 54 kg per bus, with emissions higher in the morning peak period than in the afternoon peak period. Emissions rates on the highway portion of the corridor were lower than emissions rates for the arterial portions, with mean values of 1,627 g/km and 1,993 g/km, respectively. The authors observed that the addition of each passenger influenced bus emissions per passenger differently; when the bus was less crowded, each additional passenger could decrease emissions per passenger by 7%, but that reduction becomes 1.3% when the bus is crowded. Finally, the study results estimated that operational improvements could reduce emissions by 22%, whereas switching to compressed natural gas without speed improvements could reduce emissions by 6%. The effects of emissions reduction strategies are highly dependent on the characteristics of the bus and drive cycle. These results are useful to transit planners in the selection of appropriate GHG reduction strategies as well as in the selection of candidate corridors (highway versus arterial routes) for fleet renewal.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".