Assessment of CO2 Emissions for Light-Duty Vehicles Using Dynamic Perturbation Additive Regression Trees

Abstract

Effective predictive modeling is crucial for assessing and mitigating energy consumption and CO2 emissions in light-duty vehicles (LDVs) throughout the whole value chain of an organization. This study enhances the modeling of LDV CO2 emissions by developing novel approaches to analyzing vehicle feature datasets. New tree-based machine learning models are developed to increase the accuracy and interpretability in modeling the CO2 emissions in LDVs. In particular, this study develops a new algorithm called dynamic perturbation additive regression trees (DPART). This new algorithm integrates dynamic perturbation within an iterative boosting framework. DPART progressively adjusts prediction values and explores various tree structures to improve predictive performance with reduced computation time. The effectiveness of the new ensemble-tree-based models is compared to that of other models for the vehicle emission data. The results demonstrate the new models’ capability to significantly improve predicting accuracy and reliability compared to other models. The new models also enable identifying key vehicle features affecting emissions, and thus provide valuable insights into the complex relationships among vehicle features in the dataset.