基于注意力机制的泊位占有率预测模型研究

王竹荣; 薛伟; 牛亚邦; 崔颖安; 孙钦东; 黑新宏

doi:10.11959/j.issn.1000-436X.2020241

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基于注意力机制的泊位占有率预测模型研究

学术通信 | 更新时间：2024-06-05

- 基于注意力机制的泊位占有率预测模型研究
- Research on berth occupancy prediction model based on attention mechanism
- 通信学报 2020年41卷第12期页码：182-192
- 作者机构：
  
  西安理工大学计算机科学与工程学院，陕西西安 710048
- 作者简介：
  
  [ "王竹荣（1966- ），男，湖南衡阳人，博士，西安理工大学副教授、硕士生导师，主要研究方向为智能计算、深度学习及优化应用等。" ]
  [ "薛伟（1993- ），男，陕西韩城人，西安理工大学硕士生，主要研究方向为深度学习与进化计算。" ]
  [ "牛亚邦（1993- ），男，山西吕梁人，西安理工大学硕士生，主要研究方向为深度学习。" ]
  [ "崔颖安（1975- ），男，陕西西安人，博士，西安理工大学讲师，主要研究方向为社会化媒体大数据抽样与大数据处理。" ]
  [ "孙钦东（1975- ），男，山东莒南人，西安理工大学教授、博士生导师，主要研究方向为智能信息处理、网络安全。" ]
  [ "黑新宏（1976- ），男，陕西延安人，博士，西安理工大学教授、博士生导师，主要研究方向为智能系统与安全关键系统。" ]
- 基金信息：
  
  国家重点研发计划基金资助项目(2018YFB1201500);国家自然科学基金资助项目(61773313);陕西省重点研发计划基金资助项目(2017ZDXM-GY-098);陕西省教育厅重点实验室基金资助项目(17JS100)
- DOI：10.11959/j.issn.1000-436X.2020241
  中图分类号： TP391
- 网络出版日期：2020-12，
  
  纸质出版日期：2020-12-25
- 稿件说明：
移动端阅览
王竹荣, 薛伟, 牛亚邦, 等. 基于注意力机制的泊位占有率预测模型研究[J]. 通信学报, 2020,41(12):182-192.

Zhurong WANG, Wei XUE, Yabang NIU, et al. Research on berth occupancy prediction model based on attention mechanism[J]. Journal on communications, 2020, 41(12): 182-192.
王竹荣, 薛伟, 牛亚邦, 等. 基于注意力机制的泊位占有率预测模型研究[J]. 通信学报, 2020,41(12):182-192. DOI： 10.11959/j.issn.1000-436X.2020241.

Zhurong WANG, Wei XUE, Yabang NIU, et al. Research on berth occupancy prediction model based on attention mechanism[J]. Journal on communications, 2020, 41(12): 182-192. DOI： 10.11959/j.issn.1000-436X.2020241.

摘要

为解决泊位占有率的预测精度随步长增加而下降的问题，提出了一种基于注意力机制的泊位占有率预测模型。通过卷积神经网络获得多变量的时间模式信息作为模型的注意力机制。通过对模型训练、学习特征信息，并对相关性高的序列分配较大的学习权重，来实现解码器输出高度相关的有用特征预测目标序列。应用多个停车场数据集对模型进行测试，测试结果及对比分析表明，所提模型在步长达到 36 时对泊位占有率的预测数据能较好地估计真实值，预测精度和稳定性相比LSTM均有提高。

Abstract

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.

关键词

Keywords

references

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