基于改进CNN-LSTM组合模型的分时段短时交通流预测

针对现有预测模型不能充分提取交通流时空特征的问题,提出一种基于改进卷积神经网络(convolutional neural network,CNN)和长短时记忆(long short-term memory,LSTM)神经网络的短时交通流预测方法.首先,采用分层提取方法使设计的网络结构和一维卷积核函数自动提取交通流序列的...

Short-Term Traffic Flow Prediction Method of Different Periods Based on Improved CNN-LSTM

Aiming at solving the problem that existing prediction models could not fully extract the spatio-temporal features in traffic flow, we proposed an improved convolutional neural network (CNN) with long short-term memory neural network (LSTM) for shortterm traffic flow prediction. First of all, a layered extraction method was used to design the network structure and one-dimensional convolution kernel which enabled automatic extraction of spatial features of traffic flow sequences. Second, the LSTM network modules were optimized to reduce the long-term dependence of network on the data. Finally, the optimization algorithm for rectified adaptive moment estimation (RAdam) was introduced to the end-to-end model training process, which accelerated fitting effects of the weight and improved the accuracy and robustness of network output. Experimental results showed that compared with the prediction model of stacked auto-encoders (SAEs) network, performance of the proposed model was enhanced by 3.55% and 8.82% on weekdays and weekends with model running times reduced by 6.2% and 6.9%, respectively. Compared with the prediction model of long-short term memory-support vector regression (LSTM-SVR), its performance was enhanced by 0.29% and 1.79% with model running times reduced by 9.0% and 9.7%, respectively. Therefore, the proposed model was more applicable to the short-term traffic flow prediction of different time periods.