Exploring the combined effects of major fuel technologies, eco-routing, and eco-driving for sustainable traffic decarbonization in downtown Toronto
Saba Sabet, Bilal Farooq

TL;DR
This study evaluates various vehicle technologies, traffic management, and driving behaviors through microsimulation to identify effective strategies for reducing traffic-related emissions in downtown Toronto.
Contribution
It provides a comprehensive analysis of 140 decarbonization scenarios combining fuel types, CAV penetration, and routing strategies using transformer-based models.
Findings
100% BEVs reduce GHG emissions by 75%
CAVs with anticipatory routing significantly lower GHG emissions
Eco-driving and eco-routing improve NOx emissions and travel time
Abstract
As global efforts to combat climate change intensify, transitioning to sustainable transportation is crucial. This study explores decarbonization strategies for urban traffic in downtown Toronto through microsimulation, evaluating the environmental and economic impacts of vehicle technologies, traffic management strategies (eco-routing), and driving behaviours (eco-driving). The study analyzes 140 decarbonization scenarios involving different fuel types, Connected and Automated Vehicle (CAV) penetration rates, and anticipatory routing strategies. Using transformer-based prediction models, we forecast Greenhouse Gas (GHG) and Nitrogen Oxides (NOx) emissions, along with average speed and travel time. The key findings show that 100% Battery Electric Vehicles (BEVs) reduce GHG emissions by 75%, but face challenges related to cost and infrastructure. Hybrid Electric Vehicles (HEVs) achieve…
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Taxonomy
TopicsVehicle emissions and performance · Transportation Planning and Optimization · Energy, Environment, and Transportation Policies
