Deriving Local Operating Distributions to Estimate Transit Bus Emissions Across an Urban Network
Bibliographic record
Abstract
In a 6-week data collection campaign, instantaneous bus speeds and ridership data were collected onboard 96 buses operating over 3,700 road links. Emissions were estimated by using the Motor Vehicle Emission Simulator 2014 version (MOVES2014). The effects of bus type and passenger load were explicitly accounted for in the emissions estimation process. The resulting emissions figures exhibit networkwide variations across different time periods, directions, land uses, passenger ridership figures, and transit service. Per passenger emissions data highlight the importance of considering onboard passenger weight in the estimation process. These results are relevant to transit planners who are evaluating plans to modify or introduce bus routes. This study also demonstrated a process in which local operating mode distributions were generated and specific drive cycles were developed for different average speeds and then were embedded into the MOVES2014 database. A validation test suggested that emissions figures derived using the locally developed operating mode distributions were better than and largely different from the emissions figures obtained using the MOVES default distributions. These embedded drive cycles could be useful when instantaneous speed information is unavailable, especially when developing a regional inventory for bus emissions in Montreal, Canada.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".