Decarbonizing Urban Transportation: A Case Study of Montreal
Bibliographic record
Abstract
Urban transportation is one of the largest sources of greenhouse gas emissions, responsible for almost a quarter of global CO2 output. Reducing these emissions requires tools that can capture how people actually travel across cities with fine spatial and temporal detail. In this paper, we apply a data-driven framework for Montreal that integrates an existing synthetic population dataset with multimodal routing using OpenTripPlanner and segment-level emissions estimation. Using more than 4.1 million weekday person trips, we evaluate six intervention scenarios ranging from the electrification of SUVs and pickups to ride-pooling and short-distance shifts to walking or cycling. The results show that targeting the most polluting vehicle categories can cut over 90% of their emissions, while behavioral strategies, although less impactful per trip, deliver meaningful reductions when scaled across the system. The framework is designed to balance detail, privacy, and scalability, making it transferable to other cities with limited access to high-resolution mobility data. By combining synthetic travel data, routing models, and emissions factors, it provides practical insights into both technological and behavioral pathways for decarbonizing urban mobility and supports the development of effective, evidence-based climate policies.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".