Integrating Vehicle Emission Modeling with Activity-Based Travel Demand Modeling
Bibliographic record
Abstract
An initial attempt is made to quantify vehicle emissions in the Greater Toronto Area (GTA) in Canada by exploiting travel information provided by activity-based 24-h models rather than conventional trip-based models. For this purpose, travel activity inputs to the Canadian version of the MOBILE6.2 model (MOBILE6.2C) are generated by relying on the travel demand modeling capabilities of the Travel Activity Scheduler for Household Agents (TASHA), a next-generation activity-based model of travel demand for the GTA. Additional input data supplied to MOBILE6.2C are obtained from Canadian sources and by running traffic assignment (using EMME/2) on the trip distribution matrix generated by TASHA. The integration of MOBILE6.2 with TASHA has provided estimates of the time of day that vehicle emissions occur. TASHA provides an explicit representation of trip starts and ends, which results in improved engine start emissions. Overall, because TASHA provides a better behavioral framework for modeling travel than conventional trip-based models, it is expected to lead to better emissions estimates. Such an effort also provides insight and experience that will be used later in the integration of TASHA with more advanced emission models, thus refining the reliability of practical tools that can be used to assess the environmental sustainability of policies.
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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 | 0.001 | 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) | 0.002 | 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".