Hybrid LSTM Neural Network for Short-Term Traffic Flow Prediction

The existing short-term traffic flow prediction models fail to provide precise prediction results and consider the impact of different traffic conditions on the prediction results in an actual traffic network. To solve these problems, a hybrid Long Short–Term Memory (LSTM) neural network is proposed, based on the LSTM model. Then, the structure and parameters of the hybrid LSTM neural network are optimized experimentally for different traffic conditions, and the final model is compared with the other typical models. It is found that the prediction error of the hybrid LSTM model is obviously less than those of the other models, but the running time of the hybrid LSTM model is only slightly longer than that of the LSTM model. Based on the hybrid LSTM model, the vehicle flows of each road section and intersection in the actual traffic network are further predicted. The results show that the maximum relative error between the actual and predictive vehicle flows of each road section is 1.03%, and the maximum relative error between the actual and predictive vehicle flows of each road intersection is 1.18%. Hence, the hybrid LSTM model is closer to the accuracy and real-time requirements of short-term traffic flow prediction, and suitable for different traffic conditions in the actual traffic network.