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The problem of global warming is getting more serious, and vehicle emission is the main cause. In recent years, the number of locomotives in China has been increasing at a rate of more than 20% per year, and the problem of automobile pollution is becoming more serious. The transportation industry is the main source of fossil fuel combustion and environmental pollution. Therefore, in this paper, we propose a multi-view deep neural network (MVDNN) to analyze the key factors affecting the fuel consumption of automobiles. The experiments show that the introduction of human input improves the prediction accuracy and the root mean square error (RMSE) achieves 0.993. In addition, this paper also finds that for drivers, driving habits, driving frequency, and safety awareness are the most important factors affecting the fuel consumption of vehicles by combining Lasso regression with MVDNN. Finally, by comparing the prediction accuracy of different experiments, relevant policy suggestions are made. [ABSTRACT FROM AUTHOR]

Private vehicle travel is the most basic mode of transportation, so that an effective way to control the real-world fuel consumption rate of light-duty vehicles plays a vital role in promoting sustainable economic growth as well as achieving a green low-carbon society. Therefore, the factors impacting individual carbon emissions must be elucidated. This study builds five different models to estimate the real-world fuel consumption rate of light-duty vehicles in China. The results reveal that the light gradient boosting machine (LightGBM) model performs better than the linear regression, naïve Bayes regression, neural network regression, and decision tree regression models, with a mean absolute error of 0.911 L/100 km, a mean absolute percentage error of 10.4%, a mean square error of 1.536, and an R-squared (R2) value of 0.642. This study also assesses a large pool of potential factors affecting real-world fuel consumption, from which the three most important factors are extracted, namely, reference fuel-consumption-rate value, engine power, and light-duty vehicle brand. Furthermore, a comparative analysis reveals that the vehicle factors with the greatest impact are the vehicle brand, engine power, and engine displacement. The average air pressure, average temperature, and sunshine time are the three most important climate factors. [ABSTRACT FROM AUTHOR]