A Regularized LSTM Network for Short-Term Traffic Flow Prediction

Short-term traffic flow forecast plays an important role in intelligent transportation systems. Existing traffic flow prediction model used deep layer neural network, which can't prevent over fitting, resulting in performance loss and lack of generalization ability. We propose a regularized LSTM model that fused recurrent dropout and max-norm weight constraint. We apply recurrent dropout to the recurrent connections of LSTM network, and use max-norm weight constraint to arrest the input weights not to grow very large. Simultaneously, we merge ADAM optimizer into our model. We use three datasets from different countries. In order to compare with the other researchers in the field of traffic flow prediction, we introduce same features and perform the same time interval prediction task. The experiment results show that our model has the lowest root mean square error and mean absolute error than the basic LSTM and other machine learning model including BP neural network, RNN, stacked autoencoder.