Research on berth occupancy prediction model based on attention mechanism

Zhurong WANG; Wei XUE; Yabang NIU; Ying’an CUI; Qindong SUN; Xinhong HEI

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

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Research on berth occupancy prediction model based on attention mechanism

Correspondences | 更新时间：2024-06-05

- Research on berth occupancy prediction model based on attention mechanism
- Journal on Communications Vol. 41, Issue 12, Pages: 182-192(2020)
- 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：
- 基金信息：
  
  The National Key Research and Development Program of China(2018YFB1201500);The National Natural Science Foundation of China(61773313);Shaanxi Key Research and Development Program(2017ZDXM-GY-098);Shaanxi Provincial Department of Education Key Laboratory Project(17JS100)
- DOI：10.11959/j.issn.1000-436X.2020241
  CLC： TP391
- Online First：2020-12，
  
  Published：25 December 2020
- 稿件说明：
移动端阅览
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：

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.

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references

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