Modeling Transit Bus Emissions Using <i>MOVES</i> : Comparison of Default Distributions and Embedded Drive Cycles with Local Data
Bibliographic record
Abstract
This study focuses on the comparison of operating mode distributions and other assumptions used in the estimation of transit bus emissions with the motor vehicle emission simulator (MOVES). The study area is the city of Montreal, Canada, where a single transit provider operates bus service along 220 routes. For this purpose, instantaneous speeds and passenger ridership data were collected onboard a total of 96 buses during the summer and fall of 2013. The data collection campaign covered eight bus routes in Montreal. The selected routes serve a range of corridor types capturing a variability in land use, road geometry, traffic flow, bus type, and transit service. Ultimately, the authors analyzed data from 3,702 road segments amounting to approximately 975.5 km (606 mi) with bus service. Significant differences between locally derived operating mode distributions and MOVES2014 default distributions were observed. The MOVES distributions assume a significantly larger portion of idling than that obtained from local data. The authors also investigated the drive cycle characteristics of different bus types and observed differences between standard and articulated buses, which are currently unaccounted for by MOVES. The findings illustrate the importance of collecting local bus data when estimating transit 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".