Disaggregate Level Simulation of Public Transit Emissions in a Large Urban Region
Bibliographic record
Abstract
In this study, the authors demonstrate the development of a methodology for simulating transit bus ridership and GHG emissions (in CO₂ equivalent) across a network of 200 buses in the city of Montreal, Canada. Their simulation allows them to estimate emissions for individual buses while running and idling at bus stops. The disaggregate level simulation process allows us to incorporate for each bus along every route the specificities such as vehicle type, age, fuel, and passenger load. Using MOVES2014, the authors estimated average-speed emission factors first by assuming that the MOVES default drivecycles are representative of the Montreal buses, and then by embedding operating mode distributions computed based on local drivecycles. The latter were derived from a data collection campaign conducted on-board eight transit buses. The authors observe that systemwide GHG emissions are about 15 percent lower when the MOVES default drivecycles are used. This difference could be higher on specific routes. They also investigated the effect of a 20 percent systemwide increase in ridership and observed a 1.7% increase in total emissions and a 28% decrease in per capita emissions. Finally, they estimated the effects of decreasing the frequency of low occupancy buses and increasing the frequency of high occupancy buses. These frequency changes were associated with proportional changes in emissions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".