Simulating the Environmental Effects of Isolated and Area-wide Traffic Calming Schemes Using Traffic Simulation and Microscopic Emission Modeling
Bibliographic record
Abstract
This study focuses on the development of a microscopic traffic and emission simulation system which aims at quantifying the effects of different types of traffic calming measures on vehicle emissions both at a link-level and at a network-level. It also investigates the effects of isolated traffic-calming measures at a corridor level and area-wide calming schemes, using a scenario analysis. The study is set in Montreal, Canada where a traffic simulation model for a dense urban neighborhood is extended with capabilities for microscopic emission estimation. The results indicate that on average, isolated calming measures increase carbon dioxide (CO2), carbon monoxide (CO), and nitrogen oxide (NOx) emissions by 1.50%, 0.33%, and 1.45%, respectively across the entire network. Area-wide schemes result in a percentage increase of 3.84% for CO2, 1.22% for CO, and 2.18% for NOx. Along specific corridors where traffic calming measures were simulated, increases in emissions of up to 83% were observed. These increases are mainly associated with a change in vehicle drive-cycles through increased accelerations and decelerations. The authors also account for the effect of different measures on traffic volumes and observe moderate decreases in areas that have undergone traffic calming. In spite of traffic flow reductions, total emissions do increase. Finally, they observe that speed bumps result in higher emission levels than speed humps.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".