基于CEEMD-GRU组合模型的快速路短时交通流预测

Short-term traffic flow prediction of expressway based on CEEMD-GRU combination model

为了提高短时交通流预测精度,提出了基于互补集成经验模态分解(complementary ensemble empirical mode decomposition,CEEMD)和门控循环单元(Gated Recurrent Unit,GRU)组合模型的快速路短时交通流预测方法。首先,运用互补集成经验模态分解算法,将非稳定的原始交通流时间序列数据分解为相对平稳的多个模态分量;然后,将分解后的模态分量分别建立GRU模型进行单步预测;最后,叠加每个分量的预测值,获取最终预测结果,并采用上海市南北高架快速路实测交通流数据进行实例验证。结果表明:CEEMD-GRU组合模型的预测效果明显优于GRU神经网络模型、EMD-GRU组合模型以及EEMD-GRU组合模型,平均预测精度分别提升了33.4%,25.6%和18.3%。CEEMD-GRU组合模型能够有效提取交通流数据特征分量,提高预测精度,为交通管控提供科学决策依据。

In order to improve the accuracy of short-term traffic flow prediction,a short-term traffic flow prediction method of expressway based on the combined model of complementary ensemble empirical mode decomposition(CEEMD)and gated recurrent unit(GRU)was proposed.Firstly,the unstable original traffic flow time series data were decomposed into relatively stable multiple modal components by complementary ensemble empirical mode decomposition algorithm.Then,a GRU model was established for each decomposed modal component sequence for one-step prediction.Finally,the predicted value of each component was superimposed to obtain the final prediction result,and the measured traffic flow data of north-south elevated expressway in Shanghai was used to verify and analyze the model.The experimental results show that the prediction effect of CEEMD-GRU combination model is superior to GRU neural network model,EMD-GRU combination model and EEMD-GRU combination model,and the average prediction accuracy is improved by 33.4%,25.6%and 18.3%,respectively.CEEMD-GRU combination model can effectively extract the characteristic components of traffic flow data and improve the prediction accuracy,which provides scientific decision-making basis for traffic control management.