Based on machine learning analysis of the role of new energy vehicles in reducing traffic carbon emissions

Hanpeng Cheng; Qinglin Zhang; Ye Wang

doi:10.1117/12.3026563

27 March 2024 Based on machine learning analysis of the role of new energy vehicles in reducing traffic carbon emissions

Hanpeng Cheng, Qinglin Zhang, Ye Wang

Author Affiliations +

Proceedings Volume 13105, International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2023); 131053U (2024) https://doi.org/10.1117/12.3026563
Event: 3rd International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2023), 2023, Qingdao, China

Abstract

Under the background of "double carbon", how to reduce carbon emissions is widely concerned by all walks of life. The purpose of the project is to predict and analyze the contribution of the development of new energy vehicles in China to the reduction of traffic carbon emissions by using the data provided by official organizations such as the National Bureau of Statistics and data on new energy vehicles. The grey forecast method is used to predict the quarterly increase of each factor. The following two machine learning techniques (support vector machine and ridge regression) were used. Analyze and compare the accuracy of the two models to the prediction results, and select the most accurate model for analysis. The results show that the development of new energy vehicles plays a crucial role in reducing carbon emissions.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Hanpeng Cheng, Qinglin Zhang, and Ye Wang "Based on machine learning analysis of the role of new energy vehicles in reducing traffic carbon emissions", Proc. SPIE 13105, International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2023), 131053U (27 March 2024); https://doi.org/10.1117/12.3026563

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