Vehicular Fuel Consumption and CO2 Emission Estimation Model Integrating Novel Driving Behavior Data Using Machine Learning

Fossil fuel vehicles significantly contribute to CO2 emissions due to their high consumption of fossil fuels. Accurate estimation of vehicular fuel consumption and the associated CO2 emissions is crucial for mitigating these emissions. Although driving behavior is a vital factor influencing fuel consumption and CO2 emissions, it remains largely unaddressed in current CO2 emission estimation models. This study incorporates novel driving behavior data, specifically counts of occurrences of dangerous driving behaviors, including speeding, sudden accelerating, and sudden braking, as well as driving time and driving distances on expressways, national highways, and local roads, respectively, into monthly fuel consumption estimation models for individual gasoline and hybrid vehicles. The CO2 emissions are then calculated as a secondary parameter based on the estimated fuel consumption, assuming a linear relationship between the two. Using regression algorithms, it has been demonstrated that all the proposed driving behavior data are relevant for monthly CO2 emission estimation. By integrating the driving behavior data of various vehicle categories, a generalizable CO2 estimation model is proposed. When utilizing all the proposed driving behavior data collectively, our random forest regression model achieves the highest prediction accuracy, with R2, RMSE, and MAE values of 0.975, 13.293 kg, and 8.329 kg, respectively, for monthly CO2 emission estimation of individual vehicles. This research offers insights into CO2 emission reduction and energy conservation in the road transportation sector.