Research on berth occupancy prediction model based on attention mechanism - Zhurong WANG; Wei XUE; Yabang NIU; Ying’an CUI; Qindong SUN; Xinhong HEI (Tongxin xuebao)
To solve the problem that the berth occupancy prediction accuracy decreases while the prediction step was increasing, a berth occupancy prediction model based on an attention mechanism was proposed, which was the multivariate time pattern information obtained by convolutional neural networks (CNN).The characteristic information was learned by the model training, and the sequence with higher correlation was assigned a larger learning weight, so that the highly correlated features output from the decoder could be used to predict the target sequence.Data sets of multiple parking lot were adopted to test the model.The test results show that the proposed model can estimate the real value well when the step length of berth occupancy prediction reaches 36.The prediction accuracy and stability of the model are improved compared with long short-term memory (LSTM) model.