Urban transport emission prediction analysis through machine learning and deep learning techniques

被引:0
作者
Ji, Tianbo [1 ]
Li, Kechen [1 ]
Sun, Quanwei [1 ]
Duan, Zexia [2 ]
机构
[1] Nantong Univ, Sch Transportat & Civil Engn, Nantong 226019, Jiangsu, Peoples R China
[2] Nantong Univ, Sch Elect Engn & Automat, Nantong 226019, Jiangsu, Peoples R China
关键词
Machine learning; Transport emissions; Deep learning; Air pollution; WASTE; CARBON;
D O I
10.1016/j.trd.2024.104389
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
About 6.6 million people die every year from air pollution diseases globally. Transportation industry is considered one of the leading contributors in air pollution. This research utilizes deep learning and machine learning techniques to predict China's transport-related CO2 emissions and energy needs by utilizing variables like population, car kilometers, year and GDP per capita. The outcomes have been analyzed using six analytical measures: determination coefficient, RMSE, relative RMSE, mean absolute percentage error, mean bias error and mean absolute bias error. Findings indicate that yearly increase in transport-related CO2 emissions in China will be 3.66%, and transport energy consumption will increase by 3.8%. Energy consumption and transport CO2 emissions are projected to rise by roughly 3.5 times by 2050 as compared to current levels. Therefore, government should re-evaluate its energy investment plans for the future and institute new rules, and standards regarding transport-related energy consumption and pollution reduction.
引用
收藏
页数:13
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