Decarbonizing Urban Transportation: A Case Study of Montreal

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.