Motivation and implementation of traffic management strategies to reduce motor vehicle emissions in Canadian cities
Bibliographic record
Abstract
There is a pressing need to reduce pollution emissions from transportation and consequent negative effects on air quality, public health, and the global climate. Diverse traffic management strategies have been proposed and undertaken with primary or secondary goals of reducing motor vehicle emissions. The objective of this paper is to investigate the motivation and implementation of traffic management strategies to reduce motor vehicle emissions, with a focus on moderate-scale local and regional strategies that are broadly applicable. Public documents from 44 local, regional, and provincial government entities across Canada were reviewed for information regarding the implementation of 22 traffic management strategies. Results show that different levels of government are involved in the implementation of different types of strategies, and with a different mix of traffic, safety, and environmental motivations. Regional governments more frequently cite environmental motivations and appear to be most interested in the two strategies with the strongest empirical evidence of air quality benefits: area road pricing and low emission zones. Strengthening regional transportation planning and better integrating it with municipal and provincial planning could potentially increase the implementation of effective sustainable traffic management strategies in Canada. Additional opportunities exist through emphasizing the potential environmental co-benefits of strategies such as road pricing, speed management, and traffic signal and intersection control improvements.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".