稳健性(进化)
计算机科学
采样(信号处理)
算法
弹道
高效算法
数据挖掘
计算机视觉
天文
生物化学
基因
滤波器(信号处理)
物理
化学
作者
Bozhao Li,Zhongliang Cai,Mengjun Kang,Shiliang Su,Shanshan Zhang,Lili Jiang,Yong Ge
标识
DOI:10.1080/13658816.2020.1825721
摘要
Low-sampling-rate floating car data (FCD) are more challenging than those with high-sampling-rate FCD for map matching (MM) algorithms. Some MM algorithms for low-sampling-rate FCD lack sufficient efficiency nor accuracy, especially related to complex urban road networks. This paper proposes a new method named the trajectory restoration algorithm, which is based on geometry MM algorithms to ensure efficiency and accuracy. The proposed algorithm adopts the modified A* shortest path algorithm to reduce the number of function calls and fully considers road network topology and historical matched points to improve its accuracy. We test the efficiency and accuracy of the trajectory restoration algorithm with FCD data for the complex urban road networks in Beijing. The results have strong continuity which greatly improves the utilization of FCD. We show that the proposed algorithm outperforms related MM methods in efficiency and accuracy and its robustness to restore trajectories of both high and low sampling rates in complex urban road networks.
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