基于双向RNN的私家车轨迹重构算法

肖竹; 钱鑫; 蒋洪波; 蔡成林; 曾凡仔

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

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基于双向RNN的私家车轨迹重构算法

学术论文 | 更新时间：2024-06-05

- 基于双向RNN的私家车轨迹重构算法
- Bidirectional RNN-based private car trajectory reconstruction algorithm
- 通信学报 2020年41卷第12期页码：171-181
- 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：
  
  [ "肖竹（1981- ），男，湖南涟源人，博士，湖南大学副教授、博士生导师，主要研究方向为下一代无线通信网、移动计算、测距与定位导航技术、大数据挖掘等。" ]
  [ "钱鑫（1994- ），男，安徽合肥人，湖南大学硕士生，主要研究方向为车联网、测距与定位导航技术。" ]
  [ "蒋洪波（1976- ），男，湖南长沙人，博士，湖南大学教授、博士生导师，主要研究方向为下一代无线通信网、移动计算。" ]
  [ "蔡成林（1969- ），男，湖南双峰人，博士，湘潭大学教授、博士生导师，主要研究方向为GNSS定位与导航技术。" ]
  [ "曾凡仔（1971- ），男，湖南郴州人，博士，湖南大学教授、博士生导师，主要研究方向为无线通信和车联网。" ]
- 基金信息：
  
  国家自然科学基金资助项目(U20A20181);国家自然科学基金资助项目(61732017);湖南省科技创新计划基金资助项目(2018GK2014)
- DOI：10.11959/j.issn.1000-436X.2020227
  中图分类号： TN914
- 网络出版日期：2020-12，
  
  纸质出版日期：2020-12-25
- 稿件说明：
移动端阅览
肖竹, 钱鑫, 蒋洪波, 等. 基于双向RNN的私家车轨迹重构算法[J]. 通信学报, 2020,41(12):171-181.

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.
肖竹, 钱鑫, 蒋洪波, 等. 基于双向RNN的私家车轨迹重构算法[J]. 通信学报, 2020,41(12):171-181. DOI： 10.11959/j.issn.1000-436X.2020227.

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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