交通流量经验模态分解与神经网络短时预测方法

基于经验模态分解（EMD）和神经网络，提出了一种短时交通流量预测方法。通过EMD分解把交通流量分解成不同的模态，利用神经网络对分解后的各分量进行预测，再将预测值累加得到最终的预测结果。利用EMD与神经网络模型对I-800数据库实测交通流量数据进行预测，结果表明该方法具有很高的预测精度，明显优于直接采用神经网络的预测结果。

Short-term traffic flow prediction method based on EMD and artificial neural network

An approach to short-term traffic flow prediction based on Empirical Mode Decomposition（EMD） and artificial neural network is proposed.Firstly,the traffic flow is decomposed into different modes by EMD.Then,these different modes are predicted by appropriate artificial neural networks,respectively.Finally,the traffic flow is obtained by adding up all predictive value.This method is used to predict traffic flow using 1-800 measurement data,the results show that the proposed method has high predictive accuracy,and better than the outcome of direct using neural network prediction.