Automated planning model for estimating and benchmarking road traffic carbon emissions in global cities

Abstract With the increasing urgency to mitigate greenhouse gas (GHG) emissions from urban transportation, this study presents a novel automated planning framework to estimate road traffic carbon emissions. The framework replaces traditional data-intensive and time-consuming planning model development, offering a scalable and transferable approach for coherent comparative analysis across regions worldwide. Utilizing pervasive and open data sources for network and traffic data, we infer origin–destination (O-D) travel demand and integrate it with a macroscopic emission modeling approach. A set of 45 global cities has been modeled to highlight disparities in carbon emissions, congestion, and vehicle kilometers traveled (VKT) levels against varying demand, socioeconomic, and geographic contexts. The findings suggest that while global metropolises such as New York and London exhibit pronounced increases in emissions during demand surges, cities such as Tel Aviv and Ankara demonstrate greater resilience. A comparative analysis with International Energy Agency (IEA) data confirms the validity of our estimates, revealing strong correlations in ranking trends. The findings emphasize the significant impact of congestion and VKT on urban emissions, with some cities experiencing congestion-induced emission increases of up to 300% for a mere 50% demand rise. Given these insights, the study underscores the necessity for tailored policy interventions, including enhanced public transport systems, congestion pricing, and urban mobility strategies to curb emissions effectively. By providing a rapid, scalable modeling approach, this study aids policymakers in developing targeted strategies for sustainable urban mobility and carbon reduction.