Exploring the combined effects of major fuel technologies, eco-routing, and eco-driving for sustainable traffic decarbonization in downtown Toronto
Bibliographic record
Abstract
As global efforts to combat climate change intensify, transitioning to sustainable transportation is crucial. This study investigates decarbonization strategies for urban traffic in downtown Toronto through microsimulation, focusing on eco-routing and eco-driving strategies, as well as the adoption of different fuel technologies: Battery Electric Vehicles (BEVs), Hybrid Electric Vehicles (HEVs), and conventional vehicles. A total of 140 scenarios are analyzed, incorporating varying levels of Connected and Automated Vehicle (CAV) penetration, anticipatory routing strategies, and driving behavior. Using transformer-based prediction models, the study evaluates Greenhouse Gas (GHG) and Nitrogen Oxides (NOx) emissions, average speed, and travel time. The findings demonstrate that 100% BEV adoption can reduce GHG emissions by 75%, but infrastructure and cost challenges persist. HEVs achieve moderate GHG reductions of 35%–40%, while e-fuels offer limited reductions of 5%. The study also highlights the role of eco-routing and eco-driving strategies in reducing emissions and improving travel time. However, it acknowledges potential unintended consequences, including modal shifts from active and public transportation to EVs, which could increase Vehicle Kilometers Traveled (VKT) and congestion, potentially offsetting some benefits of vehicle electrification. Integrating CAVs with anticipatory routing shows additional gains in reducing emissions and optimizing traffic flows. By providing a comprehensive evaluation of fuel technologies, traffic management strategies, and driving behaviors, this study offers actionable insights for policymakers to balance the benefits of electrification with its broader transportation impacts, supporting the development of sustainable urban mobility systems.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".