基于深度残差长短记忆网络交通流量预测算法

针对多步交通流量预测任务中时间空间特征提取效果不佳和预测未来时间交通流量精度低的问题,提出一种基于长短时记忆(LSTM)网络、卷积残差网络和注意力机制的融合模型.首先,利用一种基于编解码器的架构,通过在编解码器中加入LSTM网络来挖掘不同尺度的时间域特征;其次,构建基于注意力机制挤压激励(SE)模块的卷积残差网络嵌入到...

Traffic flow prediction algorithm based on deep residual long short-term memory network

In the multi-step traffic flow prediction task, the spatial-temporal feature extraction effect is not good and the prediction accuracy of future traffic flow is low. In order to solve these problems, a fusion model combining Long-Short Term Memory (LSTM) network, convolutional residual network and attention mechanism was proposed. Firstly, an encoder-decoder-based architecture was used to mine the temporal domain features of different scales by adding LSTM network into the encoder-decoder. Secondly, a convolutional residual network based on the Squeeze-and-Excitation (SE) block of attention mechanism was constructed and embedded into the LSTM network structure to mine the spatial domain features of traffic flow data. Finally, the implicit state information obtained from the encoder was input into the decoder to realize the prediction of high-precision multi-step traffic flow. The real traffic data was used for the experimental testing and analysis. The results show that, compared with the original graph convolution-based model, the proposed model achieves the decrease of 1.622 and 0.08 on the Root Mean Square Error (RMSE) for Beijing and New York traffic flow public datasets, respectively. The proposed model can predict the traffic flow efficiently and accurately.