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基于门控循环单元模型的在线路网匹配算法

陈良健 许建秋

陈良健, 许建秋. 基于门控循环单元模型的在线路网匹配算法[J]. 华东师范大学学报(自然科学版), 2020, (6): 63-71. doi: 10.3969/j.issn.1000-5641.201921022
引用本文: 陈良健, 许建秋. 基于门控循环单元模型的在线路网匹配算法[J]. 华东师范大学学报(自然科学版), 2020, (6): 63-71. doi: 10.3969/j.issn.1000-5641.201921022
CHEN Liangjian, XU Jianqiu. Online map matching algorithm based on the gated recurrent unit model[J]. Journal of East China Normal University (Natural Sciences), 2020, (6): 63-71. doi: 10.3969/j.issn.1000-5641.201921022
Citation: CHEN Liangjian, XU Jianqiu. Online map matching algorithm based on the gated recurrent unit model[J]. Journal of East China Normal University (Natural Sciences), 2020, (6): 63-71. doi: 10.3969/j.issn.1000-5641.201921022

基于门控循环单元模型的在线路网匹配算法

doi: 10.3969/j.issn.1000-5641.201921022
基金项目: 国家自然科学基金(61972198);江苏省自然科学基金(BK20191273)
详细信息
    通讯作者:

    许建秋, 男, 副教授, 硕士生导师, 研究方向为移动对象数据库. E-mail: jianqiu@nuaa.edu.cn

  • 中图分类号: TP392

Online map matching algorithm based on the gated recurrent unit model

  • 摘要: 路网匹配是道路网轨迹数据分析领域的一项关键技术, 一个快速且准确的路网匹配算法能够为上层应用提供良好的技术支持. 随着轨迹数据的爆炸式增长, 现有的在线路网匹配算法存在延时的现象, 尤其是在低频轨迹数据的环境下, 无法快速地对轨迹数据进行路网匹配. 神经网络和深度学习的发展为解决这些问题提供了新的方法. 提出了一种利用门控循环单元(Gated Recurrent Unit, GRU)模型快速定位轨迹采样点的候选路段、 从而加速在线路网匹配计算的方法, 并将此方法和最新的在线路网匹配算法进行了实验比较. 结果表明, 基于GRU模型的在线路网匹配算法能够有效地加快匹配过程, 提高匹配效率.
  • 图  1  GPS轨迹偏移示例

    Fig.  1  GPS track offset example

    图  2  路网匹配示意图

    Fig.  2  Demo of map matching

    图  3  基于GRU的路网匹配模型示意图

    Fig.  3  Schematic diagram of a road network matching model based on GRU

    图  4  路网匹配

    Fig.  4  Map matching

    图  5  准确率实验结果

    Fig.  5  Accuracy experimental results

    图  6  效率实验结果

    Fig.  6  Efficiency experimental results

    表  1  符号表示

    Tab.  1  Symbol definitions

    符号含义
    T轨迹
    ti轨迹的第 i 个采样点
    G道路网
    s路段
    S候选路段集
    L路径
    Li推测路径
    $l_i^*$采样点ti–1ti之间的推测路径
    下载: 导出CSV

    表  2  实验数据

    Tab.  2  Experimental data

    路段数/个轨迹条数/条采样点个数/个平均轨迹长度/km
    1 734 26938 216697 2642.8
    下载: 导出CSV
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出版历程
  • 收稿日期:  2019-06-10
  • 刊出日期:  2020-11-25

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