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Matching of vehicle GPS traces with urban road networks

机译：车辆GPS轨迹与城市道路网的匹配

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Precise positioning of moving vehicles is necessary for many applications but the accuracy of global positioning system (GPS) is seriously affected in dense urban areas. This paper presents a general framework for matching such vehicle GPS traces with urban road networks based on a pipeline approach, where the matching is divided into several sequential stages. The GPS position points with inadequate satellite geometry are first filtered by common filters of fix and horizontal dilution of precision (HDOP). Then three advanced filters are developed to filter the deviant position points that have good satellite geometry but are not consistent with the vehicle motion model, i.e. velocity, angular velocity and heading changes. Finally, two curve-to-curve distance measurement algorithms-Hausdorff and Frechet are implemented to match these filtered trajectories with urban road networks. The examination of this approach is carried out using the vehicle track logging data in Sugimoto-cho station area of Osaka city, Japan. The results show that the pipelining of several filter algorithms can remove error position points on trajectories significantly. It also suggests that the Frechet algorithm outperforms Hausdorff by including the topology of road networks
机译：在许多应用中，必须对移动车辆进行精确定位，但是在人口稠密的城市地区，全球定位系统（GPS）的准确性会受到严重影响。本文提出了一种基于管道方法将车辆GPS轨迹与城市道路网络进行匹配的通用框架，该方法将匹配分为几个连续的阶段。卫星几何形状不足的GPS位置点首先通过固定的固定滤波器和精度水平稀释（HDOP）进行过滤。然后，开发了三个高级滤波器来过滤具有良好卫星几何形状但与车辆运动模型不一致的偏离位置点，即速度，角速度和航向变化。最后，实现了两种曲线到曲线的距离测量算法-Hausdorff和Frechet，以将这些滤波后的轨迹与城市道路网络进行匹配。使用日本大阪市杉本町站区的车辆跟踪记录数据对这种方法进行了检查。结果表明，几种滤波算法的流水线处理可以显着消除轨迹上的错误位置点。这也表明，通过包括道路网络拓扑，Frechet算法的性能优于Hausdorff

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