Developing Regional 24-Hour Profiles for Link-Based, Speed-Dependent Vehicle Emissions and Zone-Based Soaks
Bibliographic record
Abstract
In this study, emissions of light-duty vehicles in the Greater Toronto Area are estimated for each hour of the day and for each of 38,000 roadway links. For this purpose, look-up tables of emission factors are derived from Mobile6.2C (the Canadian version of Mobile6.2) for various roadway speed-type combinations. In addition, 24 traffic assignments for the whole GTA are conducted using EMME2 in order to derive hourly volume and speed distributions for each link. Besides running emissions, evaporative emissions occurring during vehicle soaks are estimated at the centroids of traffic analysis zones. Soak distributions are developed using output from the Travel Activity Scheduler for Household Agents (TASHA) a next-generation, activity-based model of travel demand for the GTA. The fact that TASHA generates an explicit representation of trip starts and ends throughout the day translates into improved soak distributions. Both types of emissions are represented on a Geographic Information System platform which allows for the visualization of spatial differences in emissions and the determination of hot-spots. This methodology for allocation and representation of vehicle-induced emissions sets the stage for dispersion modeling to be conducted on a regional level and hence paves the way for the development of a comprehensive policy-support tool.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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".