Forecasting greenhouse gas emissions from road traffic in 2040 across the Greater Montreal Region of Canada
Bibliographic record
Abstract
• Background and aim: Road traffic is a major source of ambient air pollution and greenhouse gas (GHG) emissions in urban areas and is linked with human health impacts and climate change. Urban land-use planning and travel behavior greatly influence on-road traffic. The aim of this study was to map the spatial distributions of on-road traffic and its associated GHG emissions across the Greater Montreal Region (Canada) for the year 2040 under different population growth and land-use scenarios. • Methods: A random forest model was developed to estimate the annual average daily traffic (AADT) for each road segment based on the 2018 traffic count data collected from in-situ monitoring stations and Google aerial images, an origin-destination travel survey, points of interest from OpenStreetMap, and sociodemographic census data. The spatial distribution of the population in 2040 was then predicted under three population growth scenarios. According to the forecasted population distributions in 2040, we modified the population-related predictors in the 2018 random forest model to map AADT distributions for 2040. Finally, vehicle induced GHG emissions in 2040 were estimated based on AADT. • Results: Our random forest model performed well for AADT prediction, resulting in an R2 of 0.66 in leave-one-out cross-validation. Vehicle induced GHG emissions will increase 13.28% in 2040 compared to 2018 if population growth maintains past trends. GHG emissions will only increase 4.30% and 2.36% with 60% of the new population located within one kilometer of subway stations and with telecommuting increasing by 12.5% and 40% respectively. • Conclusions: Allocating new population in areas with transportation infrastructure and increasing telecommuting can reduce traffic-associated GHG emissions. Our modelling work demonstrates the potential influences of travel behaviors and land use planning on human health and climate change. • Keywords: greenhouse gas emission, traffic count, air pollution, population, land use.
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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 | 0.000 | 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".