摘要
交通排放引起的全球环境问题引起了广泛关注。传统的交通污染预测模型比较复杂,需要使用车辆排放量、交通流量、气象、地形几何等详细数据,这些数据有时是不可获取的。为了克服这一问题,在核方法的基础上,采用非线性多变量灰色模型。同时,为了提高预测交通污染程度的精度,融合滚动代谢模式。实证分析结果显示,此方法预测精度较高,具有较强的应用价值。
Global environmental problems caused by traffic emissions have been attracting wide attention. Traditional traffic pollution prediction model is complex, which needs detailed and unavailable data of vehicle emissions, traffic flow, meteorology, topographic geometry and etc. To solve the problem, the paper applies non-linear multi-variable grey model on the basis of kernel method. At the same time, to improve accuracy of traffic pollution prediction, it fuses rolling metabolic model. Empirical analysis results show the method has high prediction accuracy and strong application value.
作者
解铭
吉伟卓
齐丹媛
段冬冬
刘丽英
XIE Ming;JI Wei-zhuo;QI Dan-yuan;DUANG Dong-dong;LIU Li-ying(Handan College,Handan,Hebei 056005)
出处
《新型工业化》
2019年第10期25-27,共3页
The Journal of New Industrialization
基金
河北省社会科学基金项目(编号：HB17GL005)
关键词
灰色系统理论
交通污染
灰色多变量模型
Grey system theory
Traffic pollution
Grey multivariable model
