Evaluating vehicular emissions with an integrated mesoscopic and microscopic traffic simulation
Bibliographic record
Abstract
This paper demonstrates the implementation of a traffic simulation linked with instantaneous emissions modelling and is used to evaluate the effects of street closures and area-wide pedestrianization on vehicle-induced greenhouse gas emissions. The study is set in Montreal, Canada where traffic in a dense borough (8656 links) is simulated in a dynamic traffic assignment mode to generate second-by-second speed profiles along every link in the 7–8 AM period. Instantaneous speeds are then used to estimate link-level and intersection-level emissions. The traffic demand at the borough boundaries is simulated using a mesoscopic traffic assignment model developed for the Montreal metropolitan region (127 217 links). Street closures and area-wide pedestrianization schemes are modelled within the microscopic as well as the mesoscopic models to evaluate their effects on greenhouse gas emissions, both while accounting for changes in demand and under constant demand. In all cases, we observe an increase in borough-level greenhouse gas emissions compared to the base-case scenario indicating that such schemes do not lead to a reduction in emissions even when accounting for changes in demand. We also compare emissions calculated using the microscopic models (traffic and emissions) and those obtained from the mesoscopic assignment (traffic and average-speed emissions) and observe that the regional model is much less sensitive to local-level changes mostly due to the incapacity of simulating accelerations and decelerations and therefore underestimating the changes in borough-level emissions compared to the base-case scenario. This indicates the importance of adopting instantaneous emissions models for the evaluation of changes to street configuration.
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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) | 0.001 | 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".