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Map Matching Based on Conditional Random Fields and Route Preference Mining for Uncertain Trajectories

机译：基于条件随机场和路线偏好挖掘的不确定轨迹地图匹配

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摘要

In order to improve offline map matching accuracy of uncertain GPS trajectories, a map matching algorithm based on conditional random fields (CRF) and route preference mining is proposed. In this algorithm, road offset distance and the temporal-spatial relationship between the sampling points are used as features of GPS trajectory in a CRF model, which integrates the temporal-spatial context information flexibly. The driver route preference is also used to bolster the temporal-spatial context when a low GPS sampling rate impairs the resolving power of temporal-spatial context in CRF, allowing the map matching accuracy of uncertain GPS trajectories to get improved significantly. The experimental results show that our proposed algorithm is more accurate than existing methods, especially in the case of a low-sampling-rate.
机译：为了提高不确定GPS轨迹的离线地图匹配精度，提出了一种基于条件随机场（CRF）和路径优先挖掘的地图匹配算法。在该算法中，道路偏移距离和采样点之间的时空关系被用作CRF模型中GPS轨迹的特征，从而灵活地整合了时空上下文信息。当低GPS采样率削弱CRF中时空上下文的分辨能力时，驾驶员路线偏好还可以用于增强时空上下文，从而使不确定GPS轨迹的地图匹配精度得到显着提高。实验结果表明，我们提出的算法比现有方法更准确，特别是在低采样率的情况下。

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  • 来源
    《Mathematical Problems in Engineering》 |2015年第17期|717095.1-717095.13|共13页
  • 作者

    Xu Ming; Du Yiman; Wu Jianping; Zhou Yang;

  • 作者单位

    Beijing Univ Posts & Telecommun, Sch Comp Sci, Beijing 100876, Peoples R China.;

    Tsinghua Univ, Sch Civil Engn, Beijing 100084, Peoples R China.;

    Tsinghua Univ, Sch Civil Engn, Beijing 100084, Peoples R China.;

    Tsinghua Univ, Sch Civil Engn, Beijing 100084, Peoples R China.;

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