Predicting of Transport Carbon Dioxide Emission of Light-Duty Gasoline Vehicles Based on Driving Behavior with Machine Learning Algorithm

Driving behavior is a significant factor influencing vehicle emissions, and it must be carefully considered when modeling emissions for real road transportation vehicles. This study aims to contribute to this field by improving the intelligence and accuracy of distinguishing driving behavior volatility through the use of clustering algorithm. The research begins by processing raw emissions data collected from light-duty gasoline vehicle during real-driving emissions (RDE) test, which are used as input features for the clustering algorithm. Subsequently, a driving behavior classification method based on the gaussian mixture model (GMM) clustering algorithm is proposed. The results show that aggressive driving has a significantly higher CO₂ emission rate compared to normal and calm driving. Specifically, the average CO₂ emission rate for aggressive driving is 5.61 g/s, which is substantially higher than that of calm driving (2.40 g/s) and normal driving (2.91 g/s). Following this, the study employs Pearson correlation coefficients and an intelligent machine learning modeling approach, incorporating driving behavior–related parameters as part of the input features for the virtual CO₂ emission prediction model for light-duty gasoline vehicles. This further highlights the effectiveness of using a clustering algorithm for driving behavior classification, and it demonstrates the significant impact of driving behavior–related parameters on CO₂ emission rates, providing an accurate emission model for virtual calibration of vehicles.