Bidirectional RNN-based private car trajectory reconstruction algorithm

Zhu XIAO; Xin QIAN; Hongbo JIANG; Chenglin CAI; Fanzi ZENG

doi:10.11959/j.issn.1000-436X.2020227

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Bidirectional RNN-based private car trajectory reconstruction algorithm

Papers | 更新时间：2024-06-05

- Bidirectional RNN-based private car trajectory reconstruction algorithm
- Journal on Communications Vol. 41, Issue 12, Pages: 171-181(2020)
- 作者机构：
  
  1. 湖南大学信息科学与工程学院，湖南长沙 410082
  2. 湘潭大学信息工程学院，湖南湘潭 411105
- 作者简介：
- 基金信息：
  
  The National Natural Science Foundation of China(U20A20181);The National Natural Science Foundation of China(61732017);The Science and Technology Innovation Plan of Hunan Province(2018GK2014)
- DOI：10.11959/j.issn.1000-436X.2020227
  CLC： TN914
- Online First：2020-12，
  
  Published：25 December 2020
- 稿件说明：
移动端阅览
Zhu XIAO, Xin QIAN, Hongbo JIANG, et al. Bidirectional RNN-based private car trajectory reconstruction algorithm[J]. Journal on Communications, 2020, 41(12): 171-181.
DOI：

Zhu XIAO, Xin QIAN, Hongbo JIANG, et al. Bidirectional RNN-based private car trajectory reconstruction algorithm[J]. Journal on Communications, 2020, 41(12): 171-181. DOI： 10.11959/j.issn.1000-436X.2020227.

摘要

在复杂的城市环境中，由于存在难以避免的GNSS定位信号中断现象以及车辆行驶过程中的误差累积，易造成所收集的车辆轨迹数据不准确和不完备，因此提出一种基于双向 RNN 的私家车轨迹重构算法，使用了GNSS-OBD轨迹采集设备收集车辆轨迹信息，利用多源数据融合实现双向加权轨迹重构。同时，在轨迹重构模型中引入神经算术逻辑单元（NALU），加强深度网络的外推能力并确保轨迹预测的精度，提高算法在应对城市复杂路段时轨迹重构的稳健性；选取了实际城市路段进行了测试实验，并和现有算法进行了对比分析。通过均方根误差（RMSE）以及Google Earth对重构轨迹进行可视化展示，实验结果验证了所提算法的有效性和可靠性。

Abstract

To address the problem that in the complex urban environment

due to the inevitable interruption of GNSS positioning signal and the accumulation of errors during vehicle driving

the collected vehicle trajectory data was likely to be inaccurate and incomplete.a bidirectional weighted trajectory reconstruction algorithm was proposed based on RNN neural network.The GNSS-OBD trajectory acquisition device was used to collect vehicle trajectory information

and multi-source data fusion was adopted to achieve bidirectional weighted trajectory reconstruction.Furthermore

the neural arithmetic logic unit (NALU) was leveraged with the purpose of enhancing the extrapolation ability of deep network and ensuring the accuracy of trajectory reconstruction.For the evaluation

real-world experiments were conducted to evaluate the performance of the proposed method in comparison with existing methods.The root mean square error (RMSE) indicator shows the algorithm accuracy and the reconstructed trajectory is visually displayed through Google Earth.Experimental results validate the effectiveness and reliability of the proposed algorithm.

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references

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