Vehicular Fuel Consumption and CO 2 Emission Estimation Model Integrating Novel Driving Behavior Data Using Machine Learning

Ziyang Wang (), Masahiro Mae, Shoma Nishimura and Ryuji Matsuhashi
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Ziyang Wang: Department of Electrical Engineering and Information Systems, The University of Tokyo, Tokyo 113-8656, Japan
Masahiro Mae: Department of Electrical Engineering and Information Systems, The University of Tokyo, Tokyo 113-8656, Japan
Shoma Nishimura: Department of Digital Business Design, Aioi Nissay Dowa Insurance Co., Ltd., Tokyo 150-8488, Japan
Ryuji Matsuhashi: Department of Electrical Engineering and Information Systems, The University of Tokyo, Tokyo 113-8656, Japan

Energies, 2024, vol. 17, issue 6, 1-16

Abstract: Fossil fuel vehicles significantly contribute to CO 2 emissions due to their high consumption of fossil fuels. Accurate estimation of vehicular fuel consumption and the associated CO 2 emissions is crucial for mitigating these emissions. Although driving behavior is a vital factor influencing fuel consumption and CO 2 emissions, it remains largely unaddressed in current CO 2 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 CO 2 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 CO 2 emission estimation. By integrating the driving behavior data of various vehicle categories, a generalizable CO 2 estimation model is proposed. When utilizing all the proposed driving behavior data collectively, our random forest regression model achieves the highest prediction accuracy, with R 2 , RMSE, and MAE values of 0.975, 13.293 kg, and 8.329 kg, respectively, for monthly CO 2 emission estimation of individual vehicles. This research offers insights into CO 2 emission reduction and energy conservation in the road transportation sector.

Keywords: vehicular CO 2 emission; eco-driving; dangerous driving behavior; machine learning; random forest (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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