Short-Term Traffic Flow Prediction Method Based on Spatiotemporal Relativity
摘要
The intelligent travel of the new generation intelligent traffic system and the intelligent decision-making of traffic big data need accurate and timely short-term traffic flow prediction.Deep learning can generate features by machine learning technology,which provides a new solution to the short-term traffic prediction.Based on deep learning model,this paper proposes a short-term traffic flow prediction method that combines Convolution-Gated Recurrent Unit(Conv-GRU) and Bi-directional Gated Recurrent Unit(Bi-GRU).The proposed method uses Conv-GRU to extract the spatial feature of traffic flow and Bi-GRU to extract the periodic feature of traffic flow.The extracted features are integrated to obtain the prediction value of traffic flow.Experimental results show that the proposed method can accurately predict the short-term traffic flow.Compared with the Conv-LSTM method,this method has faster convergence speed and shorter running time.