School of Geographic Science, Nantong University, Nantong 226019, Jiangsu, China.
Sensors (Basel). 2018 May 31;18(6):1758. doi: 10.3390/s18061758.
The map matching (MM) model plays an important role in revising the locations of floating car data (FCD) on a digital map. However, most existing MM models have multiple shortcomings, such as a low matching accuracy for complex roads, long running times, an inability to take full advantage of historical FCD information, and challenges in maintaining the topological adjacency and obeying traffic rules. To address these issues, an enhanced hidden Markov map matching (EHMM) model is proposed by adopting explicit topological expressions, using historical FCD information and introducing traffic rules. The EHMM model was validated against areal ground dataset at various sampling intervals and compared with the spatial and temporal matching model and the ordinary hidden Markov matching model. The empirical results reveal that the matching accuracy of the EHMM model is significantly higher than that of the reference models regarding real FCD trajectories at medium and high sampling rates. The running time of the EHMM model was notably shorter than those of the reference models. The matching results of the EHMM model retained topological adjacency and complied with traffic regulations better than the reference models.
地图匹配（MM）模型在修正浮动车数据（FCD）在数字地图上的位置方面起着重要作用。然而，大多数现有的 MM 模型都存在多个缺点，例如复杂道路的匹配精度低、运行时间长、无法充分利用历史 FCD 信息以及在维护拓扑邻接和遵守交通规则方面存在挑战。为了解决这些问题，提出了一种增强型隐马尔可夫地图匹配（EHMM）模型，该模型采用显式拓扑表达式，利用历史 FCD 信息并引入交通规则。在不同的采样间隔下，对 EHMM 模型进行了实际地面数据集的验证，并与时空匹配模型和普通隐马尔可夫匹配模型进行了比较。实验结果表明，EHMM 模型在中等和高采样率下对真实 FCD 轨迹的匹配精度明显高于参考模型。EHMM 模型的运行时间明显短于参考模型。EHMM 模型的匹配结果在保持拓扑邻接性和遵守交通规则方面优于参考模型。
