Research on Improved Traffic Flow Prediction Network Based on CapsNet

Traffic flow prediction is the basis and key to the realization of an intelligent transportation system. The road traffic flow prediction of city-level complex road network can be realized using traffic big data. In the traffic prediction task, the limitation of the convolutional neural network (CNN) for modeling the spatial relationship of the road network and the insufficient feature extraction of the shallow network make it impossible to accurately predict the traffic flow. In order to improve the prediction performance of the model, this paper proposes an improved capsule network (MCapsNet) based on capsule network (CapsNet). First, in the preliminary feature extraction stage, a depthwise separable convolutional block is added to expand the feature channel to enrich channel information. Subsequently, in order to strengthen the reuse of important features and suppress useless information, channel attention is used to selectively reinforce learning of extended channel information so that the network can extract a large number of high-dimensional important features and improve the ability of network feature learning and expression. At the same time, in order to alleviate the feature degradation during training and the channel collapse problem easily caused by deep convolution, a shortcut connection, and a modified linear bottleneck layer structure are added to the convolution layer. The bottleneck layer adds the depth convolution and channel attention connection to the residual block of the network. Finally, the deep local feature information extracted from the improved convolutional layer is vectorized into the form of a capsule, which can more accurately model the details of road network attributes and features and improve the model expression power and prediction performance. The network is tested on the Wenyi Road dataset and the public dataset SZ-taxi. Compared with other models, the evaluation indicators of MCapsNet are better than other models in the tests of different time periods and predictors. Compared with CapsNet, the RMSE index of MCapsNet is reduced by 10.50% in the full period of Wenyi Road, 4.66% in the peak period, 9.78% in the off-peak period, and 6.07% in the SZ-tax dataset. The experimental results verify the effectiveness of the model improvement