Abstract: Ship traffic flow prediction is one of the key technologies of port intelligent transportation system,which plays a vital role in the efficiency and safety of port transportation.Aiming at the problem that the existing prediction methods are difficult to effectively extract the spatio-temporal feature information from the ship traffic flow data,a prediction method based on attention mechanism and ConvLSTM(ACLN) is proposed.ACLN first constructs a encoding network through the deep ConvLSTM to effectively extract the spatio-temporal feature information from the ship traffic flow data.Secondly,the attention mechanism is used to pay attention to the importance of the extracted spatio-temporal feature information,so that the model can automatically pay attention to the more important feature information in the process of prediction.Finally,the prediction network is constructed by multiple layers of ConvLSTM and CNN to parse the extracted spatiotemporal feature information and output the prediction result.The effectiveness of the proposed method is verified on the real port ship traffic flow data.Experimental results show that the prediction performance of the proposed method is significantly better than that of the state-of-art prediction methods,and it can perform the long and short time prediction effectively in a certain area,and has a certain practical value.

Key words: Intelligent transportation, Intelligent port, Ship flow prediction, Spatio-temporal feature extraction, Convolutional neural network, Long short-term memory network, Convolutional long short-term memory network, Attention mechanism