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An adaptive map-matching algorithm based on hierarchical fuzzy system from vehicular GPS data

机译：车载GPS数据的基于层次模糊系统的自适应地图匹配算法。

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摘要

An improved hierarchical fuzzy inference method based on C-measure map-matching algorithm is proposed in this paper, in which the C-measure represents the certainty or probability of the vehicle traveling on the actual road. A strategy is firstly introduced to use historical positioning information to employ curve-curve matching between vehicle trajectories and shapes of candidate roads. It improves matching performance by overcoming the disadvantage of traditional map-matching algorithm only considering current information. An average historical distance is used to measure similarity between vehicle trajectories and road shape. The input of system includes three variables: distance between position point and candidate roads, angle between driving heading and road direction, and average distance. As the number of fuzzy rules will increase exponentially when adding average distance as a variable, a hierarchical fuzzy inference system is then applied to reduce fuzzy rules and improve the calculation efficiency. Additionally, a learning process is updated to support the algorithm. Finally, a case study contains four different routes in Beijing city is used to validate the effectiveness and superiority of the proposed method.
机译：提出了一种改进的基于C-度量映射匹配算法的层次模糊推理方法，其中C-度量表示车辆在实际道路上行驶的确定性或概率。首先介绍了一种策略，该策略使用历史定位信息在车辆轨迹与候选道路的形状之间采用曲线-曲线匹配。通过克服仅考虑当前信息的传统地图匹配算法的缺点，提高了匹配性能。平均历史距离用于测量车辆轨迹与道路形状之间的相似性。系统的输入包括三个变量：位置点与候选道路之间的距离，行进方向与道路方向之间的角度以及平均距离。由于将平均距离作为变量添加时，模糊规则的数量将成倍增加，因此应用了层次模糊推理系统来减少模糊规则并提高计算效率。另外，学习过程被更新以支持算法。最后，通过一个包含四个不同路线的案例研究，验证了该方法的有效性和优越性。

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