ANALYTICAL EMISSION MODELS FOR SIGNALISED ARTERIALS
Bibliographic record
Abstract
ABSTRACT: Concern over the negative impact that automobiles have on air quality has prompted renewed interest in methods for quantifying vehicle tailpipe emissions. In this paper we present non-linear regression models that can be used to estimate the additional mass of carbon monoxide, hydrocarbons, and nitrogen oxides that would be expected to be produced by vehicles traversing a roadway, if a traffic signal was to be installed. The regression models use traffic demands, roadway characteristics, and traffic signal timing parameters as explanatory variables. Data for calibrating these models are obtained from the application of Integration, a microscopic traffic simulation model, to 8100 individual scenarios. The validity of using Integration as a source for emission data is examined using field data. The proposed models have adjusted R2 values ranging from 0.76 to 0.95. A comparison of the proposed models to similar models contained within the Canadian Capacity Guide indicate marked differences in the relative and absolute impact that traffic signals have on the quantity of pollutants produced. 1.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".