基于神经网络的道路交通污染物浓度预测

Modeling pollutant concentrations with artificial neural network technique

利用人工神经网络技术设计了一段式计算路侧道路交通污染物浓度的模型。首先把影响路侧道路交通污染物浓度的因素归结为3大类;然后设计了反映污染物浓度与交通流参数、气象参数和道路空间特征等因素之间数学关系的人工神经元网络结构;最后通过实际观测搜集大量的数据,对神经元网络模型进行训练,得到了精度令人满意的预测模型。

A model was developed to estimate the traffic pollutant concentrations along roadside in one step with artificial neural network technique. First, factors affecting roadside pollutant concentrations were classified into three categories. And then a structure of the artificial neural network was designed to analyze the mathematical relationship between the pollutant concentrations and the factors such as traffic flow attributes, meteorological condition and road spatial configuration. At last great amount of data were collected for training the neural network to obtain a forecasting model which had a high accuracy.