Microsimulation-Based Emissions Modeling for a Major Infrastructure Renewal Plan: Assessment of Network Attributes and Land Use Effects on Vehicular Emissions
Bibliographic record
Abstract
This study demonstrated a comprehensive microsimulation-based emissions modeling framework for a 15.37-km-long road network in the downtown core of Halifax, Canada. The study developed a sequential microscopic traffic simulation and emissions modeling tool to estimate vehicular emissions at a finer-grain spatial resolution using instantaneous speed profiles. The study evaluated the effects of a major infrastructure renewal plan that focuses on rebuilding part of the expressway in the downtown core (e.g., the replacement of multigrade signalized intersections at the Cogswell interchange with roundabouts and associated improvements). Emissions were estimated for six major criteria pollutants, including greenhouse gas (GHG), carbon monoxide (CO), nitrogen oxides (NO x ), sulfur dioxide (SO 2 ), particulate matter with a diameter of 10 µm or less (PM-10), and particulate matter with a diameter of 2.5 µm or less (PM-2.5). The results showed significant changes in emission patterns caused by the infrastructure renewal plan. The study evaluated the sensitivity of different traffic attributes as well as their combined effect on emissions’ variation. The results revealed that the plan increased emissions (from 4.246% to 28.571%) in the entire network. However, area-level evaluation suggested a reduction in emissions (from 0.018% to 19.855%) in the roundabout area compared with the multigrade signalized intersections. A land use regression model was also developed to examine the potential effect of land use and built environment attributes on emissions. The microscopic emissions model’s results will assist transportation planners in considering strategies to mitigate air pollution in the final design and implementation of the infrastructure renewal plan.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".