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.
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