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基于卡口上下文和深度置信网络的车辆轨迹预测模型研究

李暾 朱耀堃 吴欣虹 肖云鹏 吴海峰

李暾, 朱耀堃, 吴欣虹, 肖云鹏, 吴海峰. 基于卡口上下文和深度置信网络的车辆轨迹预测模型研究[J]. 电子与信息学报, 2021, 43(5): 1323-1330. doi: 10.11999/JEIT200137
引用本文: 李暾, 朱耀堃, 吴欣虹, 肖云鹏, 吴海峰. 基于卡口上下文和深度置信网络的车辆轨迹预测模型研究[J]. 电子与信息学报, 2021, 43(5): 1323-1330. doi: 10.11999/JEIT200137
Tun LI, Yaokun ZHU, Xinhong WU, Yunpeng XIAO, Haifeng WU. Vehicle Trajectory Prediction Method Based on Intersection Context and Deep Belief Network[J]. Journal of Electronics & Information Technology, 2021, 43(5): 1323-1330. doi: 10.11999/JEIT200137
Citation: Tun LI, Yaokun ZHU, Xinhong WU, Yunpeng XIAO, Haifeng WU. Vehicle Trajectory Prediction Method Based on Intersection Context and Deep Belief Network[J]. Journal of Electronics & Information Technology, 2021, 43(5): 1323-1330. doi: 10.11999/JEIT200137

基于卡口上下文和深度置信网络的车辆轨迹预测模型研究

doi: 10.11999/JEIT200137
基金项目: 国家自然科学基金(61772098),重庆市教委科技研究项目(KJQN201800641),重庆邮电大学博士高端人才项目(BYJS2017004),重庆市技术创新与应用发展专项面上项目(cstc2020jscx-msxmX0150)
详细信息
    作者简介:

    李暾:男,1980年生,高级工程师,研究方向为机器学习和网络安全

    朱耀堃:男,1994年生,硕士,研究方向为智慧交通、人工智能

    吴欣虹:女,1995年生,硕士生,研究方向为社交网络、机器学习

    肖云鹏:男,1979年生,教授,研究方向为大数据、移动互联网、信息安全

    吴海峰:男,1978年生,助理工程师,研究方向为计算机网络

    通讯作者:

    李暾 litun@cqupt.edu.cn

  • 中图分类号: TP391

Vehicle Trajectory Prediction Method Based on Intersection Context and Deep Belief Network

Funds: The National Natural Science Foundation of China (61772098), The Science and Technology Research Program of Chongqing Municipal Education Commission (KJQN201800641), The Doctoral Top Talents Program of Chongqing University of Posts and Telecommunications (BYJS2017004), Chongqing Technology Innovation and Application Development Special General Project (cstc2020jscx-msxmX0150)
  • 摘要: 针对车辆轨迹预测中节点序列的时序特性和实际路网中的空间关联性,该文提出一种基于深度置信网络和SoftMax (DBN-SoftMax)轨迹预测方法。首先,考虑到轨迹在节点集合中的强稀疏性和一般特征学习方法对新特征的泛化能力不足,该文利用深度置信网络(DBN)较强的无监督特征学习能力,达到提取轨迹局部空间特性的目的;然后,针对轨迹的时序特性,该文采用逻辑回归的预测思路,用当前轨迹集在路网特征空间中的线性组合来预测轨迹;最后,结合自然语言处理领域中的词嵌入的思想,基于实际轨迹中节点存在的上下文关系,运用节点的向量集表征了节点间的交通时空关系。实验结果表明该模型不仅能够有效地提取轨迹特征,并且在拓扑结构复杂的路网中也能得到较好的预测结果。
  • 图  1  系统框图

    图  2  DBN-SoftMax网络结构示意图

    图  3  对比不同上下文长度下、不同嵌入化维度下的模型准确率

    图  4  不同上下文长度下模型的F1值和训练时间

    图  5  对比不同上下文长度下不同算法的ROC曲线

    表  1  DBN-SoftMax算法

     输入:
      交通卡口集${{C}} = \{ {{c}}_1,{{c}}_2, ··· ,{{c}}_m\} $;
      交通轨迹${{T} } = \{ {{t} }_1,{{t} }_2, ··· ,{{t} }_m\} ,{{t} }_i = {[{{c} }_1,{{c} }_2, ··· ,{{c} }_n]^{\rm{T}}}$;
     输出:
      预测卡口编号;
     初始化$\theta = [\theta_{c1},\theta_{c2}, ··· ,\theta_{cm}],{ x} = [x_{c1},x_{c2}, ··· ,x_{cm}]$
     采样neg个负样本$({\rm{Context}}(c_0),c_0)$对每一个采样
     ($({\rm{Context}}(c_0),c_i),i = 0,1,2, ··· ,{\rm{neg}}$
     对每一个采样($({\rm{Context}}(c_0),c_i),i = 0,1,2, ··· ,{\rm{neg}}$ do:
      for $i = 1$ to $2c$:
      $\det {\rm{a}}X = 0$
       for $j = 0$ to net:
        计算$f = \sigma ({{x} }_{c0}^{\rm{T} }\theta_{c_j})$
        计算$g = (y_i - f)\eta $
        更新${\rm{deta}}X = {\rm{deta}}X + g\theta_{cj}$
        更新$\theta_{cj} = \theta_{cj} + gx_{c0}$
      end for
      更新$x_{c0} = x_{c0} + {\rm{deta}}X$
     end for
     获取参数$ {\theta ,x}$
     嵌入化轨迹集t ${ {{T} }^{'} } = \{ t_1^{'},t_2^{'}, ··· ,t_m^{'}\} ,{{t} }_i^{'} = {[x_{c1},x_{c2}, ··· ,x_{cn}]^{\rm{T}}}$
     嵌入化向量${{{t}}}_{i}^{'}$首尾拼接得到$ {v}_{i} $
     使用CD-k算法实现RBM
     DBN深度为DEPTH, RBM隐藏层的大小为RBM-SIZE
     初始化$\theta = \{ \theta_1,\theta_2, ··· ,\theta_{{\rm{DEPTH}}}\}$
     初始迭代
     for i in DEPTH, do
      for 在范围ITERATION内迭代, do:
       使用CD-k算法:
        通过式(16)计算概率分布$p(h_i|v_i;\theta_i)$
        通过式(17)计算概率分布$p(v_i|h_i;\theta_i)$
        采样获取$h_i$和$\theta_i$
        产生$\Delta w_i,\Delta b_i$
        更新$w_i = w_i + \Delta w_i,b_i = b_i + \Delta b_i$
       End for
     End for
     权重矩阵${{w}} = \{ w_1,w_2, ··· ,w_m\}$聚类得到${{{w}}^{'} } = \{ w_1^{'},w_2^{'}, ··· ,w_m^{'}\}$
     使用式(20)预测下一个卡口编号
    下载: 导出CSV

    表  2  数据样例

    车牌号通过时间卡口号纬度经度
    *A***14B2017/09/09 09:091534****798Lat1Lng1
    *D***1532017/09/14 14:49JF4****798Lat2Lng2
    *B***4332017/10/02 09:351534****342Lat3Lng3
    *C***5452017/10/24 09:51JF4****798Lat4Lng4
    *A***90O2017/11/09 13:54SH****798Lat5Lng5
    *E***M782017/11/27 18:061534****092Lat6Lng6
    下载: 导出CSV

    表  3  实验结果指标对比

    分类器深度精准率召回率F1值训练时间(s)测试时间(s)
    DBN-SoftMax10.6930.6720.6823675.1780.001
    20.7040.6860.6948792.2130.014
    30.7090.6930.7009903.2910.016
    40.7150.7020.70841093.6290.020
    50.7100.7010.70551309.8410.023
    NN-SoftMax 0.6910.6840.6875939.6200.031
    RBF SVM 0.6810.6650.6729103.89798.460
    下载: 导出CSV
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出版历程
  • 收稿日期:  2020-02-28
  • 修回日期:  2020-10-15
  • 网络出版日期:  2020-10-21
  • 刊出日期:  2021-05-18

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