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基于时空注意力卷积神经网络的交通流量预测

夏英 刘敏

夏英, 刘敏. 基于时空注意力卷积神经网络的交通流量预测[J]. 西南交通大学学报, 2023, 58(2): 340-347. doi: 10.3969/j.issn.0258-2724.20210526
引用本文: 夏英, 刘敏. 基于时空注意力卷积神经网络的交通流量预测[J]. 西南交通大学学报, 2023, 58(2): 340-347. doi: 10.3969/j.issn.0258-2724.20210526
XIA Ying, LIU Min. Traffic Flow Prediction Based on Spatial-Temporal Attention Convolutional Neural Network[J]. Journal of Southwest Jiaotong University, 2023, 58(2): 340-347. doi: 10.3969/j.issn.0258-2724.20210526
Citation: XIA Ying, LIU Min. Traffic Flow Prediction Based on Spatial-Temporal Attention Convolutional Neural Network[J]. Journal of Southwest Jiaotong University, 2023, 58(2): 340-347. doi: 10.3969/j.issn.0258-2724.20210526

基于时空注意力卷积神经网络的交通流量预测

doi: 10.3969/j.issn.0258-2724.20210526
基金项目: 国家自然科学基金(41971365);重庆市自然科学基金(cstc2019jcyj-msxm1096)
详细信息
    作者简介:

    夏英(1972—),女,教授,博士,研究方向为时空大数据,跨媒体计算等,E-mail:xiaying@cqupt.edu.cn

  • 中图分类号: U491

Traffic Flow Prediction Based on Spatial-Temporal Attention Convolutional Neural Network

  • 摘要:

    为充分挖掘交通流量的复杂时空动态相关性以提高交通流量预测精度,引入空间注意力机制与膨胀因果卷积神经网络,提出一种基于时空注意力卷积神经网络的交通流量预测模型(spatio-temporal attention convolutional neural network,STACNN). 首先,由膨胀因果卷积与门控单元构建的门控时间卷积网络模块用于获取交通流量的非线性时间动态相关性,避免在训练长时间序列时发生梯度消失或梯度爆炸;其次,采用空间注意力机制为路网中的交通传感器节点自动分配注意力权重,动态关注不相邻节点之间的空间关系,并结合图卷积神经网络提取路网的局部空间动态相关性特征;然后,通过全连接层获取最终的交通流量预测结果;最后,利用高速公路交通数据集PEMSD4、PEMSD8进行了60 min的交通流量预测实验. 实验结果表明:与基线模型中具有良好性能的时空图卷积网络(spatio-temporal graph convolutional network,STGCN)模型相比,提出的STACNN模型预测结果的平均绝对误差(mean absolute error,MAE)在两个数据集上分别提高2.79%和1.18%,平均绝对百分比误差(mean absolute percentage error,MAPE)分别提高1.00%和0.46%,均方根误差(root mean square error,RMSE)分别提高3.80%和1.25%;此外,引入的膨胀因果卷积神经网络与空间注意力机制对提取时空动态相关性特征均具有积极的贡献.

     

  • 图 1  STACNN 模型框架

    Figure 1.  STACNN model framework

    图 2  卷积核大小为2的膨胀因果卷积

    Figure 2.  Dilated causal convolution with kernel size 2

    图 3  在PEMSD4和PEMSD8上进行15、30、45、60 min流量预测的结果对比

    Figure 3.  Comparison of results of 15, 30, 45, 60-minute traffic prediction by different methods on PEMSD4 and PEMSD8

    表  1  数据集描述

    Table  1.   Dataset description

    数据集传感器数/个时间范围数据点/个
    PEMSD43072018年1月1日—
    2月28日
    16992
    PEMSD81702016年7月1日—
    8月31日
    17856
    下载: 导出CSV

    表  2  不同方法在PEMSD4和PEMSD8上进行1 h流量预测的性能对比

    Table  2.   Performance comparison of different methods for one-hour traffic prediction on PEMSD4 and PEMSD8 %

    模型PEMSD4PEMSD8
    MAEMAPERMSEMAEMAPERMSE
    HA[1]38.5628.1756.8532.0620.3447.51
    VAR[7]30.6821.5146.9225.6016.9437.51
    LSTM[11]31.7728.6544.8428.8129.6140.80
    T-GCN[16]28.0422.8141.2124.0113.9533.98
    STGCN[17]26.4516.2341.3921.9412.3233.59
    STACNN-NT24.4015.7638.4521.4212.0233.11
    STACNN-NA25.1516.2538.6521.4112.6033.10
    STACNN23.6615.2337.4020.7611.8632.34
    下载: 导出CSV

    表  3  数据集训练的时间消耗

    Table  3.   Time consumption of training on datasets s

    模型PEMSD4PEMSD8
    STGCN121.0369.20
    STACNN-NA98.7145.22
    STACNN-NT235.57110.57
    STACNN197.5290.51
    下载: 导出CSV
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出版历程
  • 收稿日期:  2021-06-28
  • 修回日期:  2022-03-01
  • 网络出版日期:  2023-01-07
  • 刊出日期:  2022-03-05

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