首页|New Machine Learning Study Results Reported from Shanghai Jiao Tong University (Integrated Moves Model and Machine Learning Method for Prediction of Co2 and No From Light-duty Gasoline Vehicle)
New Machine Learning Study Results Reported from Shanghai Jiao Tong University (Integrated Moves Model and Machine Learning Method for Prediction of Co2 and No From Light-duty Gasoline Vehicle)
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NETL
NSTL
Elsevier
Research findings on Machine Learning are discussed in a new report. According to news reporting out of Shanghai, People’s Republic of China, by NewsRx editors, research stated, “With rapid urbanization and industrialization, the number of light-duty gasoline vehicles (LDGVs) in China has continued to grow rapidly, leading to a significant increase in traffic pollution. Therefore, it is essential to accurately calculate the emission of LDGVs for air quality monitoring and management.” Financial support for this research came from National Natural Science Foundation of China (NSFC). Our news journalists obtained a quote from the research from Shanghai Jiao Tong University, “Fortunately, Motor Vehicle Emission Simulator (MOVES) is a sophisticated model for estimating mobile source emissions with good prediction accuracy. However, the parameters of MOVES are based on the field tests in the US, which is worth exploring whether MOVES can be applied to other countries. Hence, in this paper, we used the portable emission measurement system (PEMS) to conduct real driving emission (RDE) tests of LDGVs, aiming to explore the possibility of the MOVES application in China. Based on the field tests, we modified basic parameters in the MOVES model, but unsatisfactory prediction performance was obtained. Existing research on improving MOVES performance mainly involved new binning of operating modes, but these methods had limited improvements. Though studies have also used machine learning methods for predicting LDGV emissions, they lacked comparison and integration with the MOVES model. To further improve the prediction accuracy, we proposed a novel road vehicle emission model that integrated the machine learning method and the MOVES to predict the road-level emission rates of NO and CO2 emissions of LDGVs. In addition, we employed the Boruta algorithm to capture the key influencing factors and promote prediction performance. The enhanced model outperformed MOVES and achieved higher R-2 values. On average, the improvement for CO2 was 0.132, and for NO, it was 0.261.”
ShanghaiPeople’s Republic of ChinaAsiaCyborgsEmerging TechnologiesMachine LearningShanghai Jiao Tong University
NETL
NSTL
Elsevier
