School of Information Science and Technology, Northwest University, Xi'an 710127, China.
Intelligent Transportation Systems Research Center, Wuhan University of Technology, Wuhan 430070, China.
Sensors (Basel). 2020 Apr 6;20(7):2057. doi: 10.3390/s20072057.
GPS (Global Positioning System) trajectories with low sampling rates are prevalent in many applications. However, current map matching methods do not perform well for low-sampling-rate GPS trajectories due to the large uncertainty between consecutive GPS points. In this paper, a collaborative map matching method (CMM) is proposed for low-sampling-rate GPS trajectories. CMM processes GPS trajectories in batches. First, it groups similar GPS trajectories into clusters and then supplements the missing information by resampling. A collaborative GPS trajectory is then extracted for each cluster and matched to the road network, based on longest common subsequence (LCSS) distance. Experiments are conducted on a real GPS trajectory dataset and a simulated GPS trajectory dataset. The results show that the proposed CMM outperforms the baseline methods in both, effectiveness and efficiency.
GPS（全球定位系统）轨迹的低采样率在许多应用中很常见。然而，由于连续 GPS 点之间的不确定性较大，当前的地图匹配方法在低采样率 GPS 轨迹中表现不佳。在本文中，提出了一种用于低采样率 GPS 轨迹的协同地图匹配方法（CMM）。CMM 以批处理的方式处理 GPS 轨迹。首先，它将相似的 GPS 轨迹分组到聚类中，然后通过重采样来补充缺失的信息。然后，为每个聚类提取协同 GPS 轨迹，并根据最长公共子序列（LCSS）距离将其与道路网络匹配。在真实的 GPS 轨迹数据集和模拟的 GPS 轨迹数据集上进行了实验。结果表明，所提出的 CMM 在有效性和效率方面均优于基线方法。
