GRP-105 Prediction of Greenhouse Gas Emissions from Electric Vehicle Charging and Road Traffic in the United States

GRP-105 Prediction of Greenhouse Gas Emissions from Electric Vehicle Charging and Road Traffic in the United States

https://www.kennesaw.edu/ccse/events/computing-showcase/sp25-cday-program.php

Electric vehicles (EVs) are widely considered a cleaner alternative to internal combustion engine vehicles. But their growing use creates indirect emissions via two main channels: more traffic congestion from more vehicle activity and more demand on power plants providing electricity for EV charging, usually depending on fossil fuels. This work offers a comprehensive, data-driven framework to forecast greenhouse gas (GHG) emissions connected to road traffic as well as EV-related power generation. Based on vehicle and speed characteristics, we forecast vehicle-level energy consumption and emission rates using a multi-model architecture that includes a Feed Forward Neural Network (FNN). While the Meta Prophet time series model is used to project power plant emissions under different energy demand conditions, macroscopic traffic flow models are used to estimate tract-level speed-density relationships. An Integrated Emission Model combines these elements to allow assessments particular to each area. Capturing vehicle mix, traffic dynamics, and energy grid variations, our study covers four major U.S. states—California, Georgia, New York, and Washington. Results show notable spatial and temporal variations in emissions, therefore stressing the need of thorough models taking into account the intricate interactions between energy infrastructure, traffic patterns, and EV adoption.