Research on berth occupancy prediction model based on attention mechanism

被引：0

作者：

Wang Z. ^{[1
]}

Xue W. ^{[1
]}

Niu Y. ^{[1
]}

Cui Y. ^{[1
]}

Sun Q. ^{[1
]}

Hei X. ^{[1
]}

机构：

[1] School of Computer Science and Engineering, Xi'an University of Technology, Xi'an

来源：

Tongxin Xuebao/Journal on Communications | 2020年 / 41卷 / 12期

基金：

中国国家自然科学基金; 国家重点研发计划;

关键词：

Attention mechanism; Berth occupancy prediction; Sequence-to-sequence model; Time series prediction;

D O I：

10.11959/j.issn.1000-436x.2020241

中图分类号：

学科分类号：

摘要：

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. © 2020, Editorial Board of Journal on Communications. All right reserved.

引用

页码：182 / 192

页数：10

共 33 条

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