Predictive methods for CO 2 emissions and energy use in vehicles at intersections

Abstract

This study examines CO₂ emissions and vehicle energy consumption at high-traffic intersections in urban areas. Existing emission models at the macro, meso, and microscales often fail to accurately represent real traffic conditions, especially at intersections with frequent stop-and-go maneuvers. New predictive models were developed using methods such as linear regression, least absolute shrinkage and selection operator (LASSO), Ridge regression, Random Forest, and Extreme Gradient Boosting (XGBoost), with XGBoost providing the highest accuracy. The density-based spatial clustering of applications with noise (DBSCAN) algorithm was used to group data specific to intersection areas, enabling targeted analysis. Real-world driving data were collected using portable emissions measurement systems and the Hioki 3390 power analyzer. The developed models were validated and applied in simulations, including Vissim software, to improve road infrastructure planning and traffic management. These methods offer a refined approach to reducing emissions and optimizing energy use in urban transportation networks.